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# The Ultimate Guide: RNNS vs. Transformers vs. Diffusion Models

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Generated by text to image Diffusion Model

As someone who uses these tools and models extensively, I aim to unravel the complexities and nuances of RNNs, Transformers, and Diffusion Models, providing you with a detailed comparison that will illuminate the path to the right choice for your specific needs.

Whether you’re building a language translation system, generating high-fidelity images, or tackling time-series predictions, understanding the capabilities and limitations of each model is crucial. We’ll dissect the inner workings of each architecture, compare their performance across various tasks, and discuss their computational requirements.

# Understanding the Basics

Alright, let’s dive into the fascinating world of machine learning models, where algorithms become artists and data transforms into decisions. I’m talking about Recurrent Neural Networks, Transformers, and Diffusion Models — the rock stars of the AI scene. Each one with its own quirks, strengths, and a unique way of looking at the world. Understanding them is key to unlocking the potential of AI, and trust me, it’s not as daunting as it might seem.

# Sequential Data: The Unsung Hero of Information

First things first, let’s talk about sequential data. It’s everywhere, hiding in plain sight. Think about it: language, with its ordered flow of words; financial markets, with their ever-changing trends; even your daily routine, a sequence of actions you perform. All of these examples share a common thread — the order of information matters. Unlike images or individual data points where the arrangement is often irrelevant, sequential data relies heavily on the context and order of its elements.

Now, traditional neural networks, the workhorses of many machine learning tasks, struggle with this concept of order. They’re great at processing fixed-size inputs, like images, but throw a sequence at them, and they get a bit lost. They lack the “memory” to understand how past information influences the present and future.

# RNNs to the Rescue: Remembering the Past

This is where RNNs step in, like superheroes with capes made of code. They possess a unique ability — a hidden state that acts as a memory, storing information from previous inputs. Imagine it as a little notebook where the RNN jots down important details as it processes a sequence. This allows the network to understand the context and relationships between elements, making it perfect for tackling sequential data challenges.

# RNN Architectures: A Family of Sequence Masters

RNNs come in various flavors, each with its own strengths and quirks. Let’s meet the family:

**Simple RNNs: The Founding Fathers**

  * Simple RNNs are the OG members of the RNN family. They have a straightforward structure: an input layer, a hidden layer (the memory we talked about), and an output layer. Information flows through the network, with the hidden state constantly updating based on the current input and its previous value. It’s like a game of telephone where the message evolves as it’s passed along.
  * However, simple RNNs have a bit of a short-term memory problem. As sequences get longer, they struggle to retain information from the distant past, a phenomenon known as the vanishing gradient problem. This limits their effectiveness for tasks requiring long-term dependencies.



**LSTMs: The Memory Champions**

  * Long Short-Term Memory networks, or LSTMs, are the brainiacs of the RNN family. They tackle the vanishing gradient problem head-on with their sophisticated cell structure. Each LSTM cell has three gates — input, forget, and output — that control the flow of information. These gates act like tiny bouncers, deciding what information to let in, what to remember, and what to forget. This selective memory allows LSTMs to handle long-term dependencies with ease, making them ideal for tasks like language translation and speech recognition.



**GRUs: The Efficient Cousins**

  * Gated Recurrent Units, or GRUs, are like the younger, cooler cousins of LSTMs. They share a similar goal — tackling vanishing gradients — but with a simpler structure. GRUs have two gates instead of three, making them computationally more efficient than LSTMs. While they might not always match the performance of LSTMs, their speed and ease of training make them a popular choice for many applications.



# Strengths: Excelling at Sequence Data, Natural Language Processing

RNNs have proven their mettle in a wide range of applications, revolutionizing the way we interact with technology. Let’s explore some of their most impactful contributions:

**Natural Language Processing (NLP): The Language Whisperers**

  * RNNs have become the backbone of many NLP tasks. They excel at machine translation, where they can capture the nuances of different languages and generate accurate translations. Sentiment analysis, understanding the emotions behind text, is another area where RNNs shine. They can analyze reviews, social media posts, and other text data to gauge public opinion and brand sentiment.



**Time Series Analysis: Predicting the Future**

  * RNNs are natural fits for time series analysis, where data points are ordered in time. They can be used for forecasting, predicting future values based on historical trends. This is valuable in finance, weather prediction, and even predicting equipment failures in industrial settings. Additionally, RNNs can detect anomalies in time series data, identifying unusual patterns that might indicate problems or opportunities.



**Speech Recognition and Generation: Giving Machines a Voice**

  * RNNs play a crucial role in speech recognition, converting spoken language into text. They can analyze the acoustic features of speech signals and map them to corresponding words or phonemes. On the flip side, RNNs can also be used for speech generation, creating synthetic speech that sounds remarkably human-like. This technology powers virtual assistants, text-to-speech applications, and accessibility tools for people with speech impairments.



# Weaknesses: Vanishing Gradients, Limited Long-Term Memory

  * RNNs play a crucial role in speech recognition, converting spoken language into text. They can analyze the acoustic features of speech signals and map them to corresponding words or phonemes. On the flip side, RNNs can also be used for speech generation, creating synthetic speech that sounds remarkably human-like. This technology powers virtual assistants, text-to-speech applications, and accessibility tools for people with speech impairments.Weaknesses: Vanishing Gradients, Limited Long-Term Memory



But even with their impressive abilities, RNNs have limitations. As mentioned earlier, the vanilla RNNs struggle with vanishing gradients, meaning they can’t remember things too far back in the past. LSTMs and GRUs mitigate this to some extent, but long-term dependencies can still be a challenge.

Another issue is that RNNs process information sequentially, one step at a time. This can be slow, especially for long sequences. And in today’s world of big data and instant gratification, speed matters.

# Making it Concrete

Picture this: an RNN is like a conveyor belt, with input, hidden, and output layers as the workers. The weight matrices are the secret sauce that connects them all. It’s a beautiful dance of matrix multiplications and non-linear transformations.

Now, training these bad boys is no walk in the park. We use a little magic called back-propagation through time (BPTT) to make them learn. But beware, the vanishing and exploding gradient problems can be real party poopers! It’s like playing a game of telephone with numbers — the message can get lost or blow up in your face.

To give you a taste of the action, here’s a little code snippet that shows you how to create a simple RNN in PyTorch:
    
    
    import torch  
    import torch.nn as nn
    
    
    class SimpleRNN(nn.Module):  
        def __init__(self, input_size, hidden_size, output_size):  
            super(SimpleRNN, self).__init__()  
            self.hidden_size = hidden_size  
            self.rnn = nn.RNN(input_size, hidden_size, batch_first=True)  
            self.fc = nn.Linear(hidden_size, output_size)    def forward(self, x):  
            _, hidden = self.rnn(x)  
            output = self.fc(hidden.squeeze(0))  
            return output

# Transformers: The Attention Revolution

Alright folks, buckle up because we’re about to dive into the world of Transformers, the rockstars of the machine learning scene that have been turning heads and dropping jaws with their incredible capabilities. Remember those RNNs we talked about earlier? Yeah, well, Transformers came along and said, “Hold my beer,” and proceeded to revolutionize the way we handle sequential data.

# The Rise of Transformers: Attention is All You Need

So, what led to this Transformer takeover? Well, as amazing as RNNs are, they had their limitations. Remember how they process information step-by-step, like reading a book word by word? That sequential approach made it tough for them to handle long-range dependencies, where relationships between words or data points are far apart in the sequence. It’s like trying to remember what happened at the beginning of a long novel by the time you reach the end — things get a bit fuzzy.

Another issue was that RNNs could be slow and computationally expensive, especially when dealing with massive datasets. Training them felt like watching paint dry, and nobody has time for that.

Enter the **attention mechanism** , the secret sauce that makes Transformers so powerful. Instead of processing information sequentially, attention allows the model to focus on the most relevant parts of the input sequence, regardless of their position. It’s like having a superpower that lets you zoom in on the important details and ignore the distractions.

And thus, the **Transformer** was born — a novel architecture built entirely on this attention mechanism. It was like a breath of fresh air, offering a more efficient and effective way to handle sequential data. No more struggling with long-range dependencies or waiting forever for models to train. Transformers were here to stay, and they were ready to shake things up.

# Transformer Architecture: A Symphony of Attention

Let’s take a closer look at what makes these Transformers tick. Imagine a Transformer as a sophisticated machine with two main components: an **encoder** and a **decoder**. The encoder’s job is to process the input sequence, while the decoder uses that information to generate an output sequence. Think of it like a translator who listens to a sentence in one language (encoder) and then speaks the equivalent sentence in another language (decoder).

Now, the magic happens within these encoder and decoder blocks, where **self-attention** takes center stage. Self-attention allows the model to understand the relationships between different elements within the same sequence. It’s like each word in a sentence looking at the other words and figuring out how they’re connected. This helps the model grasp the context and meaning of the sequence, which is crucial for tasks like translation or text summarization.

But wait, there’s more! Transformers don’t just have one head, they have multiple heads — **multi-head attention** , to be precise. Each head focuses on different aspects of the relationships between elements, providing a more comprehensive understanding of the sequence. It’s like having a team of experts, each with their own perspective, working together to analyze the data.

# Strengths: Parallel Processing, Handling Long-Range Dependencies

Transformers come with some serious advantages:

  * **Parallel Processing:** They can process entire sequences at once, making them much faster than RNNs, especially for long sequences. Time is money, and in the AI world, that translates to efficiency and scalability.
  * **Long-Range Dependencies:** The self-attention mechanism allows Transformers to capture relationships between words that are far apart in the sequence, solving the long-term memory problem that plagued RNNs.



# Weaknesses: Computational Cost, Positional Encoding Challenges

Of course, no model is perfect, and Transformers have their own quirks:

  * **Computational Cost:** All that parallel processing and attention comes at a price. Training Transformers can require significant computational resources, which can be a barrier for those with limited hardware.
  * **Positional Encoding:** Since Transformers process sequences simultaneously, they lose the inherent order information. To compensate, they use “positional encoding” techniques to inject information about the order of the words. However, this can be tricky and may not always be perfect.



# Applications of Transformers: Conquering the World One Sequence at a Time

With their impressive capabilities, Transformers have quickly become the go-to models for a wide range of tasks, especially in the realm of natural language processing (NLP). Let’s take a look at some of the superstars that have emerged from the Transformer family:

  * **BERT (Bidirectional Encoder Representations from Transformers):** This masked language model is like a master of disguise, learning to predict missing words in a sentence. It’s become a fundamental building block for many NLP tasks, including sentiment analysis, question answering, and text classification.
  * **GPT-3 (Generative Pre-trained Transformer 3):** This language generation behemoth is like a walking encyclopedia, capable of producing human-quality text in various styles and formats. It can write stories, poems, articles, and even code, pushing the boundaries of what’s possible with AI.
  * **Vision Transformer (ViT):** Transformers aren’t just limited to text, they’ve also made their mark in the world of computer vision. ViT applies the Transformer architecture to image processing, achieving state-of-the-art results on image classification tasks.



And that’s just the tip of the iceberg! Transformers are also making waves in other domains, such as audio processing and time series analysis. They’re like the Swiss Army knives of machine learning, adaptable and effective in various situations.

# Making it Concrete

Transformers: Attention, Attention, Attention!

  * Alright, the key thing to remember is the **self-attention mechanism** , the secret sauce of Transformers.



## Building Intuition Around Self Attention

It’s like a game of “Who’s the most important word?” The query, key, and value vectors are the players, and they compute attention weights to figure out which words are the MVPs.

Transformers have multiple heads, like a hydra of attention. Each head focuses on different aspects of the input, giving the model a multi-dimensional understanding. It’s like having a team of experts working together to crack the code.

And don’t forget about positional encodings! They’re like GPS coordinates for words, making sure the model doesn’t get lost in the sequence.

Here’s a little code snippet that shows you how to use a pre-trained BERT model for sentiment analysis:
    
    
    from transformers import BertTokenizer, BertForSequenceClassification  
    import torch
    
    
    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')  
    model = BertForSequenceClassification.from_pretrained('bert-base-uncased')inputs = tokenizer("I love this movie!", return_tensors="pt")  
    labels = torch.tensor([1]).unsqueeze(0)  # Positive sentimentoutputs = model(**inputs, labels=labels)  
    loss = outputs.loss  
    logits = outputs.logitsprint(f"Sentiment: {torch.argmax(logits, dim=1).item()}")

So, there you have it — a glimpse into the world of Transformers and their attention-grabbing capabilities. They’ve revolutionized the way we handle sequential data and their impact on the field of AI is undeniable. As research and development continue, we can expect even more groundbreaking applications and advancements from these attention-powered models. The future of AI is looking bright, and Transformers are leading the way.

# Diffusion Models — Painting with Noise: A New Era in Generative AI

Now, let’s move from words to images, to the realm of creativity and artistry.

Diffusion models, the new kids on the block, are changing the game of image generation. Their approach is unique, like an artist who starts with a blank canvas and gradually adds details until a masterpiece emerges.  
Forget everything you thought you knew about creating images, because diffusion models are flipping the script and showing us a whole new way to paint with noise.

# A New Paradigm: Diffusion Models

Before we get into the nitty-gritty of how these models work, let’s take a step back and understand why they’re such a big deal.

## Generative Models: Creating New Data from Existing Patterns

Generative models, the umbrella term under which diffusion models fall, are all about creating new data that resembles the data they were trained on. Think of it like this: you show a generative model a bunch of cat pictures, and it learns the essence of “catness.” Then, it can conjure up entirely new, never-before-seen cat pictures that look like they could be real felines. Pretty cool, right?

## Diffusion Process: Gradual Addition of Noise and Reversal

Now, here’s where diffusion models get interesting. They take a unique approach to this generative process. Imagine taking a perfectly clear image and slowly adding noise to it, like static on a TV screen, until it becomes pure, unrecognizable noise. That’s the forward diffusion process.

The magic happens when we reverse this process. The diffusion model learns to take a noisy image and gradually remove the noise, step by step, until it recovers the original image. It’s like watching a skilled artist meticulously remove layers of paint to reveal a masterpiece underneath.

## Learning to Denoise: Training Diffusion Models

So, how does the model learn this denoising magic trick? We train it on a massive dataset of images. The model sees noisy images and tries to predict the less noisy versions. Over time, it gets better and better at this denoising task, essentially learning to reverse the diffusion process.

Once trained, the model can start with pure noise and gradually denoise it, step by step, until it generates a brand new image that resembles the data it was trained on. It’s like watching a sculptor chip away at a block of marble, slowly revealing a beautiful form within.

# Diffusion Model Architectures: Noise and Order

There are several different flavors of diffusion models, each with its own unique approach to denoising and image generation. Let’s explore some of the key players:

## Denoising Diffusion Probabilistic Models (DDPMs): The Trailblazers

DDPMs were among the first diffusion models to gain widespread attention. They use a Markov chain to model the diffusion process, meaning each step in the noise addition or removal depends only on the previous step. This makes them relatively simple to implement and train.

## Cascaded Diffusion Models: Divide and Conquer

Cascaded diffusion models break down the denoising process into multiple stages, each handled by a separate model. This allows for more fine-grained control over the generation process and can lead to higher-quality images. It’s like having a team of specialists working together to create a masterpiece.

## Score-Based Generative Models: Riding the Probability Waves

Score-based models take a slightly different approach. Instead of directly predicting the denoised image, they estimate the gradient of the data distribution at each step of the diffusion process. This gradient, also known as the score, tells the model which direction to move in to remove noise and get closer to the real data distribution. It’s like navigating with a compass, always pointing towards the desired destination.

# Strengths: Generating High-Quality Images, Flexible and Creative Applications

Diffusion models are making waves in the creative world for good reason:

  * **High-Quality Images:** They can generate incredibly realistic and high-quality images, often indistinguishable from real photographs. It’s like having an AI artist at your fingertips, able to create anything you can imagine.
  * **Flexible and Creative Applications:** Diffusion models are not limited to generating images from scratch. They can also be used for tasks like image in-painting (filling in missing parts of an image), image-to-image translation (changing the style or content of an image), and even generating 3D models.



# Weaknesses: Training Complexity, Potential for Bias and Artifacts

However, diffusion models also have their challenges:

  * **Training Complexity:** Training these models requires a deep understanding of the diffusion process and careful optimization of the various parameters. It’s not for the faint of heart.
  * **Potential for Bias and Artifacts:** Like any model trained on data, diffusion models can reflect and amplify biases present in the training data. It’s important to be aware of these biases and take steps to mitigate them. Additionally, they can sometimes generate artifacts or unrealistic details in the generated images.



# Making it Concrete

Diffusion Models: Noise, Noise, Baby!

  * It’s like watching a painter create a masterpiece, one brushstroke at a time. The forward diffusion process is like adding noise to a pristine image until it’s unrecognizable. The reverse diffusion process is like the artist carefully removing the noise, revealing the hidden beauty underneath.



Under the hood, it’s all about the objective function. The model is trained to minimize the variational lower bound or the noise conditioning score. It’s like playing a game of “Guess Who?” with noise.

Here’s a code snippet that shows you how to generate images using a pre-trained diffusion model:
    
    
    from diffusers import DDPMPipeline, DDIMScheduler
    
    
    model_id = "google/ddpm-cifar10-32"  
    scheduler = DDIMScheduler(beta_start=0.0001, beta_end=0.02, beta_schedule="linear", num_train_timesteps=1000)  
    pipeline = DDPMPipeline.from_pretrained(model_id)image = pipeline(num_inference_steps=1000, output_type="numpy").images[0]

# Wrapping Up: Finding The Right Fit — Without Overfitting 😜

Alright, folks, let’s cut to the chase. We’ve waltzed through the theoretical ballroom of RNNs, Transformers, and diffusion models, admiring their unique moves and capabilities. Now, it’s time to get down to business and answer the burning question: which one do you pick for your next project?

If you’re expecting a simple answer, a magic formula to spit out the perfect model every time, well, prepare for disappointment. This ain’t no vending machine where you punch in your desires and out pops a perfectly wrapped solution. Choosing the right model is an art, not a science, and it demands a discerning eye, a bit of experience, and a willingness to get your hands dirty.

**No Silver Bullets in the Model Armory**

First things first: ditch the notion of a one-size-fits-all model. Each of these architectures comes with its own baggage, its own quirks and predilections. RNNs, with their looping mechanisms, excel at handling sequences, but they can get tripped up by long-term dependencies and vanishing gradients. Transformers, the cool kids on the block, boast parallel processing and attention mechanisms that conquer long sequences, but they can be computationally demanding and require careful positional encoding. And then there are diffusion models, the artists of the bunch, conjuring up high-quality images from noise, but they come with training complexities and the potential for biases and artifacts.

It’s like picking the right tool for a job. You wouldn’t use a sledgehammer to hang a picture frame, nor would you attempt to build a house with a screwdriver. Each tool has its purpose, its strengths and limitations. The same goes for our model menagerie.

# Comparison: The Showdown

  * Alright, let’s put these models in the ring and see how they stack up against each other. Here’s a little comparison table to make things crystal clear:



As you can see, each model has its own strengths and weaknesses. RNNs are the OGs, great for short-term memories. Transformers are the new kids on the block, with their fancy self-attention mechanisms. And diffusion models? They’re the wild cards, shaking up the image generation game.

But here’s the thing: with great power comes great computational responsibility. Transformers and diffusion models can be real resource hogs, especially during training. It’s like trying to stuff an elephant into a mini-fridge — it’s not gonna be pretty.

**Problem and Resources: The Guiding Stars**

So, how do we navigate this model maze? It starts with a clear understanding of two crucial factors: the problem you’re trying to solve and the resources at your disposal.

**Task at Hand:**

Is it sequence modeling? Predicting the next word in a sentence, forecasting stock prices, or analyzing time-series data? RNNs, especially LSTMs and GRUs, might be your go-to guys.

Dealing with natural language processing? Machine translation, text summarization, or sentiment analysis? Transformers, with their self-attention superpowers, are likely to take the crown.

Dreaming up stunning images or generating creative content? Diffusion models are the Picassos of the AI world, ready to turn noise into masterpieces.

**Resource Reality Check:** Data is the lifeblood of these models. If you’re working with limited data, RNNs might struggle to learn effectively, and Transformers might succumb to the overfitting demons. In the realm of big data, however, both Transformers and diffusion models can truly shine, learning complex patterns and relationships.

But data isn’t the only piece of the puzzle. Computational resources are equally crucial. Training these models, especially the larger Transformer and diffusion models, can demand significant computing power and time. Be realistic about the hardware you have access to and the time you can afford to invest in training. Remember, a model that takes forever to train might not be practical, no matter how impressive its results.

**Skills and Ecosystem: The Supporting Cast**  
Beyond the core factors of the problem and resources, there are other elements to consider.

**Framework Familiarity:** Are you a PyTorch aficionado or a TensorFlow devotee? Thankfully, all three model types have robust support in major deep learning frameworks, but your familiarity with a specific framework might influence your choice.

**Learning Curve:** Let’s face it, none of these models are a walk in the park. Each comes with its own set of complexities and theoretical underpinnings. Understanding the underlying mechanisms is crucial for effective application and troubleshooting. Consider your own comfort level and willingness to invest time in learning the intricacies of each architecture.

**Community and Support:** No man is an island, and that’s especially true in the ever-evolving world of AI. A strong community and readily available resources can be invaluable when you hit a roadblock or need inspiration. Look for models with active communities, comprehensive documentation, and plenty of online tutorials and examples.

**The Ever-Shifting Sands of AI**  
Remember, this landscape is far from static. New architectures are emerging, existing models are being refined, and the capabilities of AI are expanding at a breakneck pace. What’s cutting-edge today might be old news tomorrow. Staying up-to-date with the latest advancements is essential to make informed decisions and leverage the full potential of AI.

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url: https://pmc.ncbi.nlm.nih.gov/articles/PMC9250736/
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            A comparative study on deep learning models for text classification of unstructured medical notes with various levels of class imbalance - PMC
        

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# A comparative study on deep learning models for text classification of unstructured medical notes with various levels of class imbalance

[Hongxia Lu](https://pubmed.ncbi.nlm.nih.gov/?term=%22Lu%20H%22%5BAuthor%5D)

### Hongxia Lu

1Schmid College of Science and Technology, Chapman University, 1 University Dr, Orange, CA 92866 USA 

Find articles by [Hongxia Lu](https://pubmed.ncbi.nlm.nih.gov/?term=%22Lu%20H%22%5BAuthor%5D)

1, [Louis Ehwerhemuepha](https://pubmed.ncbi.nlm.nih.gov/?term=%22Ehwerhemuepha%20L%22%5BAuthor%5D)

### Louis Ehwerhemuepha

1Schmid College of Science and Technology, Chapman University, 1 University Dr, Orange, CA 92866 USA 

2Children’s Health of Orange County (CHOC), Orange, CA 92868 USA 

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1,2, [Cyril Rakovski](https://pubmed.ncbi.nlm.nih.gov/?term=%22Rakovski%20C%22%5BAuthor%5D)

### Cyril Rakovski

1Schmid College of Science and Technology, Chapman University, 1 University Dr, Orange, CA 92866 USA 

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1Schmid College of Science and Technology, Chapman University, 1 University Dr, Orange, CA 92866 USA 

2Children’s Health of Orange County (CHOC), Orange, CA 92868 USA 

✉

Corresponding author.

Received 2021 Aug 21; Accepted 2022 May 23; Collection date 2022.

© The Author(s) 2022

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/). The Creative Commons Public Domain Dedication waiver ([http://creativecommons.org/publicdomain/zero/1.0/](https://creativecommons.org/publicdomain/zero/1.0/)) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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PMCID: PMC9250736 PMID: [35780100](https://pubmed.ncbi.nlm.nih.gov/35780100/)

## Abstract

### Background

Discharge medical notes written by physicians contain important information about the health condition of patients. Many deep learning algorithms have been successfully applied to extract important information from unstructured medical notes data that can entail subsequent actionable results in the medical domain. This study aims to explore the model performance of various deep learning algorithms in text classification tasks on medical notes with respect to different disease class imbalance scenarios.

### Methods

In this study, we employed seven artificial intelligence models, a CNN (Convolutional Neural Network), a Transformer encoder, a pretrained BERT (Bidirectional Encoder Representations from Transformers), and four typical sequence neural networks models, namely, RNN (Recurrent Neural Network), GRU (Gated Recurrent Unit), LSTM (Long Short-Term Memory), and Bi-LSTM (Bi-directional Long Short-Term Memory) to classify the presence or absence of 16 disease conditions from patients’ discharge summary notes. We analyzed this question as a composition of 16 binary separate classification problems. The model performance of the seven models on each of the 16 datasets with various levels of imbalance between classes were compared in terms of AUC-ROC (Area Under the Curve of the Receiver Operating Characteristic), AUC-PR (Area Under the Curve of Precision and Recall), F1 Score, and Balanced Accuracy as well as the training time. The model performances were also compared in combination with different word embedding approaches (GloVe, BioWordVec, and no pre-trained word embeddings).

### Results

The analyses of these 16 binary classification problems showed that the Transformer encoder model performs the best in nearly all scenarios. In addition, when the disease prevalence is close to or greater than 50%, the Convolutional Neural Network model achieved a comparable performance to the Transformer encoder, and its training time was 17.6% shorter than the second fastest model, 91.3% shorter than the Transformer encoder, and 94.7% shorter than the pre-trained BERT-Base model. The BioWordVec embeddings slightly improved the performance of the Bi-LSTM model in most disease prevalence scenarios, while the CNN model performed better without pre-trained word embeddings. In addition, the training time was significantly reduced with the GloVe embeddings for all models.

### Conclusions

For classification tasks on medical notes, Transformer encoders are the best choice if the computation resource is not an issue. Otherwise, when the classes are relatively balanced, CNNs are a leading candidate because of their competitive performance and computational efficiency.

### Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01665-y.

**Keywords:** Medical notes, Text classification, BERT, CNN, Deep learning, Embedding, Transformer encoder

## Background

Unstructured medical notes such as discharge summaries are valuable health records that contain rich clinical information about patients’ health conditions. Some of the disease details may not be reflected in the structured data fields. Many studies have been carried out to extract additional information from unstructured medical notes and make mortality predictions based on these data alone [1–4]. This study aims to explore the model performance of various deep learning algorithms in text classification tasks on medical notes to help point the attention of the research community to the potentials of text classification and the behaviors of various NLP (Natural Language Processing) algorithms on medical notes data in different class imbalance scenarios. The algorithms compared in this study include traditional recurrence networks such as RNN, GRU, LSTM, Bi-LSTM, as well as CNN, and attention algorithms such as the Transformer encoder and BERT-Base. The model performances were evaluated in terms of AUC-ROC, AUC-PR, F1 Score, and Balanced Accuracy. Deep learning algorithms such as RNN, GRU, LSTM, and Bi-LSTM models are typically used for NLP tasks and have achieved promising results [5–7]. They are designed to work with sequence data by allowing previous outputs to be used as inputs which allows flow of information from previous elements (and posterior elements in Bi-LSTMs) of the sequence.

GRUs, LSTMs, and Bi-LSTMs are advanced variants of the vanilla RNNs with additional gates (mathematical operations involving additional weights to be trained) added in an RNN unit to overcome the vanishing or exploding gradient problem that RNNs often suffer with long sequences. LSTMs have two additional gates compared to GRUs which entails a better performance on long sequences by allowing information from further back to be carried over to the current unit. Bi-LSTMs have an additional layer (on top of the LSTM units) that goes backwards so that the information from posterior elements is passed on to previous units. This feature works particularly well for text data since the context (information from both previous elements and from posterior elements) is important for interpretation of words. The extra gates and layer, however, consequently result in more complexity and longer training time.

More recently, CNNs have attracted attention for NLP tasks due to their superior performance especially on lengthy texts [8–11]. CNNs are widely used in computer vision such as image classification or image recognition [12, 13]. CNNs in computer vision feature a 2-Dimensional or 3-Dimensional convolutional layer that extracts information from neighboring pixels and thus recognizes patterns across space. CNNs in NLP tasks employ a 1-Dimensional convolutional layer which extracts information from adjacent words. It is not quite clear exactly why CNN outperforms the traditional NLP algorithms such as RNN, GRU, LSTM, and Bi-LSTM in many cases but it is widely accepted that the number of kernels in the 1-dimensional convolutional layer in CNN serves as the n-gram (n adjacent words treated as one) technique in NLP [14–16]. In traditional NLP algorithms, anything more than 3-g would be too cumbersome. CNN algorithms, however, can easily adopt an 8-g or even higher gram technique (depending on the length of the text) without increasing the computational cost.

Transformers have been successfully applied to many NLP tasks since the introduction of the Transformer by Vaswani et al. [17]. The Transformer model is a novel network architecture that is based solely on attention mechanisms, dispensing with recurrence and convolutions entirely [17]. Transformers for tasks such as translation or question answering have both encoders and decoders, while Transformers for text classification tasks typically have only encoders. An encoder has two layers, a multi-head self-attention layer and a feedforward layer. Unlike the recurrent networks which process the words sequentially by taking the information from the previous word as input for the processing of the current word, the Transformer processes an input sequence as a whole. Another novel design of the Transformer is that it introduces positional embedding which captures information from the order of words. The positional embeddings are added to the word embeddings before they are fed to the encoder. One major disadvantage of Transformers is their high computational cost especially when the text sequences are long. Longer sequences are disproportionately expensive because attention is quadratic to the sequence length due to the self-attention of each word with every other word in the sequence [18].

BERT (Bi-directional Encoder Representations from Transformers) is a transformer-based language representation model which was designed to pre-train deep bidirectional representations from unlabeled text from BooksCorpus and English Wikipedia [18]. Two architectures of the BERT model (BERT-Base and BERT-Large) were introduced in the original paper. The BERT-Base model has 12 Transformer encoders, 12 self-attention heads in each encoder, a hidden size of 768, and a total of 110 M parameters. The BERT-Large model has 24 Transformer encoders, 16 self-attention heads in each encoder, a hidden size of 1024, and a total of 340 M parameters. The BERT model achieved state-of-the-art performance on a number of natural language understanding tasks when it was published. It has been successfully applied in many NLP tasks since then [19–21]. One major drawback of BERT is the costly computational resources needed to train or fine-tune the model due to the large number of parameters [22].

### Data

In this study, we used de-identified discharge summary data made available by Harvard University in 2008 for a challenge to classify obesity and its comorbidities with multiple classes (presence, absence, or questionable) for each disease that were annotated with textual judgments and intuitive judgments, respectively [23]. The data consist of 1,237 unique discharge summaries from the Partners HealthCare Research Patient Data Repository and were annotated from a list of 16 disease conditions by three experts from the Massachusetts General Hospital [24]. The literature on classification tasks using this dataset is focused on optimizing the macro-F score of the multi-class classification task by primarily employing rule-based methods (or rule-based methods combined with traditional machine learning algorithms such as SVM) which involved heavy text preprocessing that are tailored for these specific discharge summaries in association with these 16 diseases [24]. For example, Ware et al. employed the Apelon terminology engine to provide synonym sets for drug names and used Domain Specific Language (DSL) to frame the rules to identify the presence of a disease [25]. Yang et al. built a dictionary for diseases, symptoms, treatments, medications, and their synonyms [26]. Solt et al. also developed a regular expression driven string replacement dictionary for all occurrences of relevant abbreviations, synonyms, plain English equivalents, spelling variants, frequent typos, suffixed forms, etc. [27].

The goal of this study is to compare the behavior of the 7 deep learning algorithms in terms of their performance on the same datasets, their training efficiency, and their ability to handle imbalanced classes, as well as the effect of two types of word embedding approaches. Therefore, we simplified the multi-class task into a binary-class problem for the intuitive labels only and applied general text preprocessing. We converted the data into 16 datasets for binary classifications, each with the same 1,237 discharge summaries but a different binary outcome variable denoting the presence or absence of a particular disease. The disease prevalence of the 16 disease conditions in the datasets are listed in Table 1. The disease prevalence ranges from 5 to 73%, with hypertriglyceridemia being the least prevalent and hypertension the most prevalent. The disease prevalence also reflects the class imbalance level between the positive (disease presence) and negative (disease absence) classes in our binary classification problems.

#### Table 1.

Disease prevalence (_N_ = 1,237)

Disease | Disease Prevalence | Disease | Disease Prevalence  
---|---|---|---  
Hypertriglyceridemia | 5% | GERD* | 20%  
Venous Insufficiency | 7% | Depression | 20%  
Asthma | 13% | Obesity | 40%  
Gout | 13% | CHF* | 43%  
OSA* | 14% | Hypercholesterolemia | 47%  
PVD* | 15% | CAD* | 55%  
Gallstones | 15% | Diabetes | 66%  
OA* | 18% | Hypertension | 73%  
  
[Open in a new tab](table/Tab1/)

_OSA_ * obstructive sleep apnea, _PVD_ * peripheral vascular disease , _OA_ * osteo arthritis, _GERD_ * gastroesophageal reflux disease, _CHF_ * congestive heart failure, _CAD_ * coronary artery disease

The discharge summary notes in the dataset include contents such as the description of the current illness, medical history, information about physical examination and laboratory examination, treatment or services provided if applicable, and discharge medications. These unstructured medical notes require special treatment before they can be fed into deep learning algorithms. We first converted all words to the lower case so that words such as “disease” and “Disease” are treated as the same word. We then removed numbers and punctuations which do not carry significant information about the diagnoses. Standard stop words (the most common words in any natural language which do not add much value in NLP modeling) such as “the”, “this”, “that” were removed as well as template words such as “discharge”, “admission”, “date”, and words with only one or two characters such as “mg”. Detailed descriptive statistics of the variables denoting the number of words and characters in the discharge summaries before and after cleaning are shown in Table 2. In particular, the average number of words before and after cleaning were 1170 and 557 with a minimum of 146 and 50 and a maximum of 4280 and 2098, respectively. Similarly, the average number of characters before and after cleaning were 6870 and 4429 with a minimum of 903 and 410 and a maximum of 25,842 and 16,976, respectively.

#### Table 2.

Descriptive statistics

Descriptive Statistics | Number of Words | Number of Characters  
---|---|---  
**Before Cleaning** | **After Cleaning** | **Before Cleaning** | **After Cleaning**  
Minimum | 146 | 50 | 903 | 410  
25% Percentile | 819 | 391 | 4798 | 3089  
Median | 1084 | 517 | 6391 | 4098  
Mean | 1170 | 557 | 6870 | 4429  
75% Percentile | 1425 | 687 | 8404 | 5420  
Maximum | 4280 | 2098 | 25,842 | 16,976  
Standard Deviation | 506 | 242 | 2960 | 1931  
  
[Open in a new tab](table/Tab2/)

The dataset was then randomly split (stratified according to the disease label) into training and test sets containing 75% and 25% of the data, respectively, for 10 iterations and the average metrics from the 10 iterations were used for comparison of the model performance. In each iteration, the dataset was randomly split with stratification and the models were trained on the same training set and tested on the same test set. Regular tokenizing with Keras Tokenizer was performed to convert text into numbers for all models except for BERT which uses a different tokenization technique (WordPiece tokenization) [18, 28]. The regular tokenizing procedure takes the following two steps. First, it creates a word-index dictionary based on word frequency in the training set so that every unique word is assigned an integer value as the index (an integer between 1 and the maximum number of unique words in the texts. 0 is reserved for padding.) Then, it transforms each text to a sequence of integers by taking each word in the discharge summary note, looking it up in the word-index dictionary, and replacing it with its corresponding index. Next, the medical notes in the test set were converted to sequences of integers by looking up each word in the word-index dictionary previously constructed from the training set. The reason that the word-index dictionary is built based on the training set only is to avoid information leaking from the test set, because the test set is supposed to contain new data that the model has never seen. At this point, all medical notes have been converted to numbers but they are of different lengths because each discharge note has different length. We arbitrarily chose the maximum sequence length to be 557 (the average length of the sequences in the dataset) and forced all sequences to be of the same length by truncating the longer sequences and padding the shorter sequences with 0’s. After these preprocessing steps, the original discharge summary notes have been transformed into sequences of integers of the same length and ready to be fed into the deep learning models. For the pre-trained BERT-Base model, the maximum sequence length allowed is 512.

## Methods

A CNN model with eight 1-dimensional filters and a kernel size of eight, a RNN model with eight units, a GRU model with eight units, a LSTM model with eight units, a Bi-LSTM model with eight units, and a Transformer encoder with one encoder and two self-attention heads were fit on all 16 datasets with a batch size of 32 and 20 epochs. A pre-trained BERT-Base model with a 128-unit feed-forward layer before the classification output layer was also fit with a batch size of 32 and 3 epochs. All models had a Word Embedding layer (word representations to capture the similarity between words) with an input length of 557 and an output dimension of 200. The BERT-Base model had both word embeddings and positional embeddings of dimension 512 by 768.

Since the focus of this study was to compare the performance of different models instead of optimizing a specific model, we tried to use the same and/or default hyperparameters for all models. There have been many studies and debates about the choice of hyperparameters [29–31]. The batch size can be any number between 1 and the number of samples in the training set. There are many factors that could affect the optimal choice of the batch size such as available computational resource, the size of the data, the choice of the optimizer and the learning rate. Generally speaking, the larger the batch size, the more likely the algorithm is to converge to the global minimum but more memory is required during the training process. When the batch size is too small, the model is more prone to noisiness and thus requires smaller learning rate for stability which results in more training steps and thus longer training time. There can be a sweet spot but it is dataset and model specific and requires a trial and error search. Commonly used batch sizes are 16, 32, and 64 for small and moderate-sized datasets. We chose to use 32 because our dataset was relatively small and the input sequences were long. Similar to the choice of the batch size, the choice of the number of epochs is also dataset and model specific. Larger number of epochs requires longer training time and could result in overfitting while small number of epochs could result in underfitting. We chose to use 20 epochs for all models except for BERT (3 epochs). The authors of BERT recommended 2–4 epochs for fine-tuning BERT [18]. Dropout was used to mitigate the overfitting problem. Commonly used dropout rates are between 0.1 and 0.5. We chose to use 0.3 for all models. For other hyperparameters such as learning rate, activation function, and optimizer, we chose to use the default values for all models.

Table 3 shows the architecture information of the seven models. The models were evaluated in terms of AUC-ROC (Area Under the Curve of the Receiver Operating Characteristic), AUC-PR (Area Under the Curve of Precision and Recall), F1 Score, and Balanced Accuracy (detailed reports on these metrics including Precision, Recall, and Specificity are shown in Table 4–9 in the Appendix).

### Table 3.

Model architectures

Model | Number of Filters/Units/Encoders | Embedding Dimension | Max Sequence Length | Dropout | Activation Function | Optimizer | Total Parameters  
---|---|---|---|---|---|---|---  
CNN | 8 | 200 | 557 | 0.3 | ReLU | Adam | 5.51 M  
RNN | 8 | 200 | 557 | 0.3 | ReLU | Adam | 5.50 M  
GRU | 8 | 200 | 557 | 0.3 | ReLU | Adam | 5.50 M  
LSTM | 8 | 200 | 557 | 0.3 | ReLU | Adam | 5.50 M  
Bi-LSTM | 8 | 200 | 557 | 0.3 | ReLU | Adam | 5.51 M  
Transformer Encoder | 1 encoder (2 heads) | 200 | 557 | 0.3 | ReLU | Adam | 5.94 M  
BERT-Base | 12 encoders (12 heads) | 768 | 512 |  0.3 (fine-tune layer) | ReLU (fine-tune layer) | Adam (fine-tune layer) | 110 M  
  
[Open in a new tab](table/Tab3/)

Word embeddings have replaced the traditional Bag-of-Words (BoW) representations (e.g. TF-IDF, Count Vectorization) and have become essential in NLP tasks. They are the projection of tokenized word vectors onto a real-valued embedding matrix that are learned from the data during training or pre-trained on large datasets. One advantage of word embeddings over BoW is their significantly lower dimension that at most equals the maximum length of the input sequences (versus the maximum number of unique words in the dataset for BoW). Another advantage is their dense feature (less zeros for word embeddings compared to the sparse feature of BoW) [32]. Pre-trained word embeddings are widely used in NLP tasks with small datasets because they better capture the semantic and syntactic meaning of a word since they are trained on large datasets. Many different models for creating pre-trained word embeddings such as Word2Vec, GloVe, fastText, and BioWordVec among others have been developed. In this study, we implemented the GloVe embeddings (dimension 200) and the BioWordVec embeddings (dimension 200) on all models except for the Transformer encoder and BERT-Base and no pre-trained word embeddings for all models (word embeddings of dimension 200 learned from data during training). Pre-trained word embeddings were not used on the Transformer encoder and BERT-Base in this study because they are not a priority in the design of Transformers and BERT additionally requires special tokens for input sequences such as [CLS] and [SEP]. GloVe was trained on five corpora of varying sizes, including Wikipedia, Gigaword and web data from Common Crawl5 [33]. BioWordVec embeddings were trained on biomedical text and a biomedical controlled vocabulary called Medical Subject Headings (MeSH) to accommodate for the NLP needs in the biomedical domain [34].

## Results

Figure 1 (a, b, c, d) shows the AUC-ROC, AUC-PR, F1 Score, and Balanced Accuracy of the seven models for all 16 datasets ordered from lowest to highest according to the disease prevalence. The Transformer encoder produced the highest AUC-ROC for 13 datasets (the highest of the 13 is 0.926 for Diabetes), the highest AUC-PR for 14 datasets (the highest of the 14 is 0.954 for Diabetes), the highest F1 Score for 15 datasets (the highest of the 15 is 0.905 for Diabetes), and the highest Balanced Accuracy for 14 datasets (the highest of the 14 is 0.939 for Diabetes). CNN exceeded the Transformer encoder for 3 datasets in terms of AUC-ROC (0.702 for Hypertriglyceridemia, 0.883 for CHF, and 0.882 for CAD), 2 datasets in terms of AUC-PR (0.171 for Hypertriglyceridemia, and 0.897 for CAD), 1 dataset in terms of F1 Score (0.822 for CAD), and 2 datasets in terms of Balanced Accuracy (0.823 for CAD, and 0.863 for Hypertension). The values of the F1 Score and/or the Balanced Accuracy were unavailable for some models due to zero values for the True Positives (actual disease presence correctly predicted) which was encountered when the disease prevalence was less than 20%. The zero values for the True Positives occur when the algorithm predicts all positive cases as negative. Similarly, when a good portion of the positive cases (which can be a very small number in a small sample) are misclassified, the AUC-PR, F1 score, and the Balanced Accuracy would be low, while the AUC-ROC and the overall accuracy can still be high. This is not surprising because when the classes are highly imbalanced and the sample size is small, there are not many minority cases for the algorithms to learn the distinct characteristics of the minority class, and the cost for misclassifying minority cases is small even when all minority cases are misclassified.

### Fig. 1.

[![Fig. 1](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07e7/9250736/64263e7d5885/12874_2022_1665_Fig1_HTML.jpg)](https://www.ncbi.nlm.nih.gov/core/lw/2.0/html/tileshop_pmc/tileshop_pmc_inline.html?title=Click%20on%20image%20to%20zoom&p=PMC3&id=9250736_12874_2022_1665_Fig1_HTML.jpg)

[Open in a new tab](figure/Fig1/)

Model Performance. (**c**. F1 Score* and **d**. Balanced Accuracy*: some points in these graphs are missing due to NA values resulted from zero values for the True Positives in the highly imbalanced datasets)

Sordo et. al. reported that, as the sample size increases, machine learning algorithms (Naïve Bayes, SVM, and Decision Trees) show a substantial improvement in performance in predicting the smoking status of a patient from text excerpts extracted from narrative medical reports [35]. This is reasonable because the algorithms need to “learn” the latent information in the data by being exposed to a large enough amount of data in both classes. Given our small sample size (a total of 1,237 lengthy medical notes) and the fact that deep learning algorithms are data hungry, the performance of the Transformer encoder and the CNN algorithm are quite promising on the relatively balanced datasets. The datasets on which the Transformer encoder and the CNN algorithm performed poorly were all highly imbalanced and the other five models performed even worse on them. The low performance on the imbalanced datasets is a result of having too few samples in the minority class for the algorithms to learn from, or the number of minority cases in the training set not big enough to represent the minority class in the test set.

Table 4 shows how small the sample sizes are in the minority class in the training set, especially when the classes are highly imbalanced. For the datasets where the disease prevalence is less than 10%, the number of samples in the minority class in the training set is no more than 62 which is challenging for any algorithm to perform well. As shown in Fig. 1 (b, c, d), as the prevalence (or the number of samples in the minority class) increases, the performance of all models improved substantially in terms of AUC-PR, F1 score, and the Balanced Accuracy. Therefore, when used on big enough datasets (with a large enough number of samples in both classes), these algorithms could render excellent performance.

### Table 4.

Number of samples in each class in training and test sets

Disease | Prevalence | Training Set | Test Set  
---|---|---|---  
Disease Presence Presence | Disease Absence | Disease Presence | Disease Absence  
Hypertriglyceridemia | 5% | 50 | 878 | 17 | 292  
Venous Insufficiency | 7% | 62 | 865 | 21 | 289  
Asthma | 13% | 123 | 805 | 41 | 268  
Gout | 13% | 120 | 808 | 40 | 269  
OSA | 14% | 129 | 799 | 43 | 266  
PVD | 15% | 135 | 793 | 45 | 264  
Gallstones | 15% | 141 | 787 | 47 | 262  
OA | 18% | 168 | 760 | 56 | 253  
GERD | 20% | 184 | 743 | 62 | 248  
Depression | 20% | 187 | 741 | 62 | 247  
Obesity | 40% | 374 | 554 | 125 | 184  
CHF | 43% | 402 | 526 | 134 | 175  
Hypercholesterolemia | 47% | 432 | 496 | 144 | 165  
CAD | 55% | 512 | 416 | 170 | 139  
Diabetes | 66% | 616 | 312 | 205 | 104  
Hypertension | 73% | 677 | 250 | 226 | 84  
  
[Open in a new tab](table/Tab4/)

Figure 2 displays the training time of all seven algorithms averaged over all 16 classification tasks (on a computer with Intel(R) Core(TM) i7-6560U CPU @ 2.20 GHz, and 16 GB RAM for). The CNN model ran consistently faster than all other models in all tasks. On average, CNN ran faster by as much as 17.6% than RNN which was the second fastest algorithm, 91.3% faster than the Transformer encoder and 94.7% faster than BERT-Base. The BERT-Base model is the most time consuming due to the large number of parameters being fed to the top layer even though it was only fine-tuned using 3 epochs.

### Fig. 2.

![Fig. 2](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07e7/9250736/507032c07c88/12874_2022_1665_Fig2_HTML.jpg)

[Open in a new tab](figure/Fig2/)

Average training time

Figure 3 shows the model performance with and without pre-trained word embeddings. There was slight improvement in terms of F1 Score, and Balanced Accuracy for RNN, GRU, and LSTM with both the GloVe and BioWordVec embeddings when the disease prevalence is greater than 50%. BioWordVec embeddings performed slightly better than GloVe embeddings in most cases, and the improvement is the most significant for the Bi-LSTM model. For the CNN model, the performance is better without pre-trained word embeddings.

### Fig. 3.

[![Fig. 3](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07e7/9250736/c95c27d1850d/12874_2022_1665_Fig3_HTML.jpg)](https://www.ncbi.nlm.nih.gov/core/lw/2.0/html/tileshop_pmc/tileshop_pmc_inline.html?title=Click%20on%20image%20to%20zoom&p=PMC3&id=9250736_12874_2022_1665_Fig3_HTML.jpg)

[Open in a new tab](figure/Fig3/)

Model performance with and without Pre-trained Word Embeddings. (**c**. F1 Score* and **d**. Balanced Accuracy*: some points in these graphs are missing due to NaN values resulted from zero values for the True Positives in the highly imbalanced datasets)

Figure 4 shows the training time of the models with and without pre-trained word embeddings averaged over all iterations and all datasets. Both pre-trained word embeddings shortened the training time significantly for all models. Models with the GloVe embeddings ran the fastest. The training time of Bi-LSTM was shortened by as much as 61% with the GloVe embeddings than without pre-trained embeddings.

### Fig. 4.

[![Fig. 4](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07e7/9250736/5d391ee704c0/12874_2022_1665_Fig4_HTML.jpg)](https://www.ncbi.nlm.nih.gov/core/lw/2.0/html/tileshop_pmc/tileshop_pmc_inline.html?title=Click%20on%20image%20to%20zoom&p=PMC3&id=9250736_12874_2022_1665_Fig4_HTML.jpg)

[Open in a new tab](figure/Fig4/)

Average training time with and without pre-trained word embeddings

## Discussion

Medical notes are often lengthy and thus constitute high-dimensional data. High-dimensional data (with a relatively small sample size) can be challenging due to its inevitable overfitting problems. The longest discharge summary in our data had 2,098 words after cleaning while the sample size was only 1,237. This issue was mitigated by the following treatments of the data. First, after tokenizing, the discharge notes of different lengths were forced into the same arbitrary length (557 in our study) by truncating the longer notes and padding the shorter notes with 0 s. There was no formula for the choice of the length, but Ying Wen et al. reported that a length that is close to the average length of the texts in the training set generally produces better results. If the length is too small, it will result in a great loss of information; if the length is too large, it will lead to sparse data in shorter notes and will include more noise from the longer notes [32]. Second, a relatively small number of epochs (20 epochs) was used to train the models to avoid the models memorizing the training data which is another source of overfitting with high-dimensional data.

In this study, the Transformer encoder stood out among all models in nearly all class imbalance scenarios. Its strength lies in the self-attention feature where all other words in the sequence are considered at once when encoding a specific word which effectively resolves the issue of “forgetting” information from previous words in long sequences that recurrent networks often encounter. CNN also outperformed the other five models and achieved a comparable performance to the Transformer encoder when the disease prevalence is close to or greater than 50%. Somewhat surprisingly, BERT-Base, as a powerful NLP model, performed poorly in all scenarios. It is likely due to the fact that it was trained on general text, not on medical text and thus failed to capture the information and the relationships of medical words. In addition, the maximum sequence length allowed in BERT-Base is 512 while the average length of the discharge summary notes in the dataset is 557 which could lead to too much loss of information.

The results for the five models with different word embeddings show that the BioWordVec embeddings slightly improved the performance of the Bi-LSTM model for some datasets. In general, models with BioWordVec embeddings performed slightly better than those with GloVe embeddings which is reasonable since the BioWordVec embeddings were trained on biomedical text while Glove embeddings were trained on corpora that are not in the biomedical domain. The reason that the improvement with BioWordVec was not quite noticeable may be due to the fact that many medical words (especially medication names such as zanflex, fondapurinox, diurhesis) and mis-spelled words (such as dopthromycin, anestheteic, amoxicil) in the dataset were still not recognized by BioWordVec. As expected, even more words were not recognized by GloVe which presumably contributed to the faster training time.

When the classes were highly imbalanced (disease prevalence lower than 30% or higher than 70%), all models performed poorly with very low AUC-PR and very low F1 Score and Balanced Accuracy (or even no available F1 Score and/or Balanced Accuracy). This is mainly due to unequal misclassification costs where misclassifying the minority class does not result in too much cost. This is especially true when the dataset is small and there are too few samples in the minority class to matter when misclassified. When the dataset is large, under-sampling the majority class, over-sampling the minority class, or a combination of the two can be used to balance the classes.

For small datasets, if collecting more data from the minority class is not feasible, using data augmentation (SMOTE, back-translation, random swap, and random deletion, etc.) to increase the number of samples in the minority class may help improve the performance of the models [19, 23–25]. In addition, for lengthy text data, the length of the text sequence to be fed into the model may also affect model performance since a short sequence may lose too much information and a long sequence may result in sparse data and introduce more noise as well as longer training time and overfitting problems [36–38]. If medical expert consultation is available, applying text preprocessing methods such as those implemented in some of the top 10 i2b2 challenge solutions should also help improve the model performance [24]. For example, abbreviations are very common in medical notes, and expanding them should help improve the results since otherwise they are often treated as unknown words and thus not contributing any information. In addition, most drug names are also not recognized and tagging drug names that are strongly indicative of a certain disease will also help improve prediction accuracy.

## Conclusion

In the binary text classification tasks studied, the Transformer encoder stood out among all algorithms studied (in terms of model performance metrics such as AUC-ROC, AUC-PR, F1 Score, and Balanced Accuracy). When the classes were more balanced, the CNN model performed equally well with markedly shorter training time. When the dataset was highly imbalanced with the positive class (disease presence) as the minority, AUC-ROC may be inflated, and AUC-PR may be a more reliable metric to evaluate model performance. In turn, when the dataset is highly imbalanced with the negative class (disease absence) as the minority, AUC-ROC may be a more accurate measure of model performance. In addition, domain specific pre-trained word embeddings such as BioBERT [39] and ClinicalBERT [40, 41] may help yield better results since the word embeddings are trained on medical text using the powerful BERT model. In summary, for classification tasks on medical notes, Transformer encoders are the best choice if the computation resource is not an issue. Otherwise, when the classes are relatively balanced, CNNs are a leading candidate because of its comparable performance and computational efficiency.

## Supplementary Information

[12874_2022_1665_MOESM1_ESM.docx](/articles/instance/9250736/bin/12874_2022_1665_MOESM1_ESM.docx) (142.1KB, docx) 

**Additional file 1:** **Appendix: Table 4.** Model Performance Evaluation Metrics (Average of 10 Iterations). **Table 5.** Model Performance Evaluation Metrics (Average of 10 Iterations) - CNN with/without Pre-trained Word Embeddings. **Table 6.** Model Performance Evaluation Metrics (Average of 10 Iterations) - RNN with/without Pre-trained Word Embeddings. **Table 7.** Model Performance Evaluation Metrics (Average of 10 Iterations) - GRU with/without Pre-trained Word Embeddings. **Table 8.** Model Performance Evaluation Metrics (Average of 10 Iterations) - LSTM with/without Pre-trained Word Embeddings. **Table 9.** Model Performance Evaluation Metrics (Average of 10 Iterations) - Bi-LSTM with/without Pre-trained Word Embeddings.

## Acknowledgements

Not applicable.

## Abbreviations

AI
    

Artificial Intelligence

NLP
    

Natural Language Processing

CNN
    

Convolutional Neural Network

RNN
    

Recurrent Neural Network

GRU
    

Gated Recurrent Unit

LSTM
    

Long Short-Term Memory

Bi-LSTM
    

Bi-directional Long Short-Term Memory

BERT
    

Bi-directional Encoder Representations from Transformers

BoW
    

Bag-of-Words

AUC-ROC
    

Area Under the Curve of the Receiver Operating Characteristic

AUC-PR
    

Area Under the Curve of Precision and Recall

OSA
    

Obstructive Sleep Apnea

PVD
    

Peripheral Vascular Disease

OA
    

Osteo Arthritis

GERD
    

Gastroesophageal Reflux Disease

CHF
    

Congestive Heart Failure

CAD
    

Coronary Artery Disease

SMOTE
    

Synthetic Minority Oversampling Technique

## Authors’ contributions

HL aggregated, managed and quality checked the medical discharge summary notes; carried out the AI model fitting, summarized the results, and wrote sections of the manuscript. LE reviewed and analyzed the results. CR analyzed the data, interpreted the results, wrote sections of the manuscript, and provided guidance and insights in this study. All authors read and approved the final manuscript.

## Funding

This study received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

## Availability of data and materials

The data that support the findings in this study are derived from datasets publicly available after simple steps for approval on <https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/>. The code for this study is available on <https://github.com/Hanna520/medical_notes_AI.git>.

## Declarations

### Ethics approval and consent to participate

This study used deidentified data from the Harvard Website and followed all ethical considerations instituted by the providers of the data. No participants are involved in this study.

### Consent for publication

Not applicable.

### Competing interests

The authors declare no competing interest regarding this article.

## Footnotes

**Publisher’s Note**

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

## Contributor Information

Hongxia Lu, Email: holu@chapman.edu.

Louis Ehwerhemuepha, Email: lehwerhemuepha@choc.org.

Cyril Rakovski, Email: rakovski@chapman.edu.

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## Associated Data

_This section collects any data citations, data availability statements, or supplementary materials included in this article._

### Supplementary Materials

[12874_2022_1665_MOESM1_ESM.docx](/articles/instance/9250736/bin/12874_2022_1665_MOESM1_ESM.docx) (142.1KB, docx) 

**Additional file 1:** **Appendix: Table 4.** Model Performance Evaluation Metrics (Average of 10 Iterations). **Table 5.** Model Performance Evaluation Metrics (Average of 10 Iterations) - CNN with/without Pre-trained Word Embeddings. **Table 6.** Model Performance Evaluation Metrics (Average of 10 Iterations) - RNN with/without Pre-trained Word Embeddings. **Table 7.** Model Performance Evaluation Metrics (Average of 10 Iterations) - GRU with/without Pre-trained Word Embeddings. **Table 8.** Model Performance Evaluation Metrics (Average of 10 Iterations) - LSTM with/without Pre-trained Word Embeddings. **Table 9.** Model Performance Evaluation Metrics (Average of 10 Iterations) - Bi-LSTM with/without Pre-trained Word Embeddings.

### Data Availability Statement

The data that support the findings in this study are derived from datasets publicly available after simple steps for approval on <https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/>. The code for this study is available on <https://github.com/Hanna520/medical_notes_AI.git>.

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# How Transformers Outperform RNNs in NLP and Why It Matters

![](https://appinventiv.com/wp-content/uploads/2019/12/chirag-150x150.png)

Chirag Bharadwaj

_AVP - Technology_

February 19, 2025

![Transformer vs. RNN](https://appinventiv.com/wp-content/uploads/2024/07/how_transformers_outperform_rnns_in_nlp_and_why_it_matters.webp)

Table of Content

  * Understanding RNN Architecture - Their Advantages and Limitations
  * The Limitations of RNNs
  * The Emergence of Transformers in NLP - A Triumph Over RNN
  * Working Mechanism of Transformer Architecture - A Perk Over RNN
  * Comparative Analysis: Transformer vs RNN
  * NLP Transformers in Action: A World of Applications
  * Speech Recognition
  * Sentiment Analysis
  * Text Summarization
  * Named Entity Recognition (NER)
  * Question-Answering (QA)
  * Machine Translation
  * Conversational AI/Chatbots
  * Text Generation
  * Text-to-Text Generation
  * Examples of Transformer NLP Models
  * GPT and ChatGPT
  * BERT
  * LaMDA
  * T5
  * Future of NLP Transformers - Redefining the AI Era
  * Enter into the Golden Era of NLP with Appinventiv
  * FAQs



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A major goal for businesses in the current era of artificial intelligence (AI) is to make computers comprehend and use language just like the human brain does. Numerous advancements have been made toward this goal, but Natural Language Processing (NLP) plays a significant role in achieving it.

[NLP](https://appinventiv.com/blog/natural-language-processing-applications-for-business/), a key part of AI, centers on helping computers and humans interact using everyday language. This field has seen tremendous advancements, significantly enhancing applications like machine translation, [sentiment analysis](https://appinventiv.com/blog/ai-sentiment-analysis-in-business/), question-answering, and voice recognition systems. As our interaction with technology becomes increasingly language-centric, the need for advanced and efficient NLP solutions has never been greater.

Recurrent Neural Networks (RNNs) have traditionally played a key role in NLP due to their ability to process and maintain contextual information over sequences of data. This has made them particularly effective for tasks that require understanding the order and context of words, such as language modeling and translation. However, over the years of NLP’s history, we have witnessed a transformative shift from RNNs to Transformers.

The rise of Transformers over RNNs was not random. RNNs, designed to process information in a way that mimics human thinking, encountered several challenges. In contrast, Transformers in NLP have consistently outperformed RNNs across various tasks and address its challenges in language comprehension, text translation, and context capturing.

The emergence of Transformers has revolutionized the field of NLP. This new model in AI-town redefines how NLP tasks are processed in a way that no traditional machine learning algorithm could ever do before. Let’s dive into the details of Transformer vs. RNN to enlighten your [artificial intelligence ](https://appinventiv.com/guide/artificial-intelligence-in-business/)journey.

![Get started on NLP journey with Appinventiv](https://appinventiv.com/wp-content/uploads/2024/07/how_transformers_outperform_rnns_in_nlp_and_why_it_matters-3.webp)

## Understanding RNN Architecture - Their Advantages and Limitations

RNN in NLP is a class of neural networks designed to handle sequential data. Unlike traditional feedforward neural networks, RNNs have connections that form directed cycles, allowing them to maintain a memory of previous inputs. This makes RNNs particularly suited for tasks where context and sequence order are essential, such as language modeling, speech recognition, and time-series prediction.

For example, in language modeling, RNNs can predict the next word in a sentence based on the words that came before it, thanks to their ability to capture temporal dependencies.

### The Limitations of RNNs

Even though RNNs offer several advantages in processing sequential data, it also has some limitations.

#### Vanishing and Exploding Gradient Problems

One of the significant challenges with RNNs is the vanishing and exploding gradient problem. During training, the gradients used to update the network's weights can become very small (vanish) or very large (explode), making it difficult for the network to learn effectively.

#### Long-Term Dependency Issues

The vanishing and exploding gradient problem intimidates the RNNs when it comes to capturing long-range dependencies in sequences, a key aspect of language understanding. This limitation of RNN makes it challenging for the models to handle tasks that require understanding relationships between distant elements in the sequence.

#### Computational Inefficiency

RNNs process sequences sequentially, which can be computationally expensive and time-consuming. This sequential processing makes it difficult to parallelize training and inference, limiting the scalability and efficiency of RNN-based models.

Let’s understand the limitations of RNN in NLP through an example – suppose if RNN has to translate an English sentence into French — “Appinventiv is a leading app development company, formed in 2014”, the RNN would process this sentence (input) word-by-word, and sequentially give the French synonyms of each word. This output can lead to irrelevancy and grammatical errors, as in any language, the sequence of words matters the most when forming a sentence.

These limitations in RNN models led to the development of the Transformer - An answer to RNN challenges.

## The Emergence of Transformers in NLP - A Triumph Over RNN

The Transformer architecture NLP, introduced in the groundbreaking paper "Attention is All You Need" by Vaswani et al., has revolutionized the field of Natural Language Processing. Unlike previously dominant RNN-based models like LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit), Transformers do not rely on recurrence or convolution, instead using a self-attention mechanism that allows for more efficient processing of sequential data.

This innovation has led to significant improvements in both the performance and scalability of NLP models, making Transformers the new standard in the AI town.

![ NLP Transformer Architecture](https://appinventiv.com/wp-content/uploads/2024/07/how_transformers_outperform_rnns_in_nlp_and_why_it_matters-1.webp)

### Working Mechanism of Transformer Architecture - A Perk Over RNN

Transformers’ self-attention mechanism enables the model to consider the importance of each word in a sequence when it is processing another word. This self-attention mechanism allows the model to consider the entire sequence when computing attention scores, enabling it to capture relationships between distant words. This capability addresses one of the key limitations of RNNs, which struggle with long-term dependencies due to the vanishing gradient problem.

Additionally, transformers for natural language processing utilize parallel computing resources to process sequences in parallel. This parallel processing capability drastically reduces the time required for training and inference, making Transformers much more efficient, especially for large datasets.

Based on the Transformers applications and working mechanisms, they can be divided into three prime categories – Encoder, Decoder, and Encoder-Decoder. Here is a table illustrating these three main types of Transformers in NLP, along with their examples and use cases:

Category| Description| Examples| Use Cases  
---|---|---|---  
Encoder| Encode input data into contextual representations to understand the entire input sequence.| BERT, RoBERTa, ALBERT| 

  * Text classification
  * Named entity recognition
  * Sentiment analysis
  * Question answering

  
Decoder| Generate text by predicting the next word in the sequence based on previous words.| GPT-1, GPT-2, GPT-3, GPT-4, Transformer-XL| 

  * Text generation
  * Conversational AI
  * Language modeling
  * Text completion

  
Encoder-Decoder| Transform one sequence into another by encoding the input and decoding it into an output sequence.| Original Transformer, T5, BART| 

  * Machine translation
  * Text summarization
  * Text-to-text generation
  * Sequence-to-sequence tasks

  
  
## Comparative Analysis: Transformer vs RNN

To understand the advancements that Transformer brings to the field of NLP and how it outperforms RNN with its innovative advancements, it is imperative to compare this advanced NLP model with the previously dominant RNN model.

![Transformer Model](https://appinventiv.com/wp-content/uploads/2024/07/how_transformers_outperform_rnns_in_nlp_and_why_it_matters-7.webp)

Here is a brief table outlining the key difference between RNNs and Transformers.

Aspect| RNNs| Transformers  
---|---|---  
Efficiency and Performance| Sequential processing; computationally expensive| Parallel processing; highly efficient  
Training Time and Resources| Longer training time; cannot be parallelized, less resource-intensive| Faster training on large datasets; higher memory usage. (GPT-3 was trained on 45TB of data)  
Handling of Dependencies| Struggles with long-term dependencies| Excels at capturing long-range dependencies  
Model Size and Scalability| Smaller models; Difficult to scale; limited by sequential nature| Larger models; Highly scalable; handle large datasets well  
Adaptability to Various NLP Tasks| Good for specific tasks (e.g., language modeling)| Versatile; excels in diverse NLP applications  
Parallelization| Limited parallelization due to sequential nature.| Excellent parallelization capabilities, leading to faster training and inference  
Scalability| Low scalability due to sequential processing and efficiency issues.| Highly scalable, able to handle large models and datasets efficiently  
Contextual Understanding| Inefficient in capturing context over long sequences| Superior contextual understanding, especially for long-range dependencies.  
Attention Mechanism| Lacks inherent attention mechanism; requires additional layers for attention.| Built-in self-attention mechanism, enhancing the model's ability to focus on relevant parts of the input  
  
## NLP Transformers in Action: A World of Applications

Still unsure about the advantages and applications of Transformer vs. RNN? Well, if the comparison between the Transformer and RNN listed above has not clarified your dilemma, the wide array of applications mentioned below will provide a deeper understanding. Natural language processing with Transformers has revolutionized the field of NLP with their ability to handle complex language tasks. Here are some key applications and use cases of Transformers for natural language processing:

![Transformers Use Cases - Key NLP Tasks](https://appinventiv.com/wp-content/uploads/2024/07/how_transformers_outperform_rnns_in_nlp_and_why_it_matters-4.webp)

### Speech Recognition

Speech recognition, also known as speech-to-text, involves converting spoken language into written text. Transformer-based architectures like Wav2Vec 2.0 improve this task, making it essential for voice assistants, transcription services, and any application where spoken input needs to be converted into text accurately. Google Assistant, Apple Siri, etc., are some of the prime examples of speech recognition.

You may like reading: **[Cost of developing an AI Voice and TTS app like Speechify](https://appinventiv.com/blog/cost-to-build-app-like-speechify/)**

![Speech recognition in NLP](https://appinventiv.com/wp-content/uploads/2024/07/how_transformers_outperform_rnns_in_nlp_and_why_it_matters-2.webp)

### Sentiment Analysis

Transformers for natural language processing can also help improve [sentiment analysis](https://appinventiv.com/blog/ai-sentiment-analysis-in-business/) by determining the sentiment expressed in a piece of text. Transformers like BERT, ALBERT, and DistilBERT can identify whether the sentiment is positive, negative, or neutral, which is valuable for market analysis, brand monitoring, and analyzing customer feedback to improve products and services.

Here is an exemplary sentence to illustrate the process:

_“The coffee at the cafe was good, but the service was awful”._

This sentence has mixed sentiments that highlight the different aspects of the cafe service. Without the proper context, some language models may struggle to correctly determine sentiment. On the other hand, Transformers in NLP capture context more effectively.

Revised Sentence: _" The ambiance of the cafe was nice, but the coffee was awful; I wouldn't go back."_ Here, the overall sentiment is clearly negative.

### Text Summarization

Text summarization involves creating a concise summary of a longer text while retaining its key information. Transformer models such as BART, T5, and Pegasus are particularly effective at this. This application is crucial for news summarization, content aggregation, and summarizing lengthy documents for quick understanding.

### Named Entity Recognition (NER)

Named Entity Recognition (NER) is the process of identifying and classifying entities such as names, dates, and locations within a text. When performing NER, we assign specific entity names (such as I-MISC, I-PER, I-ORG, I-LOC, etc.) to tokens in the text sequence. This helps extract meaningful information from large text corpora, enhance search engine capabilities, and index documents effectively. Transformers, with their high accuracy in recognizing entities, are particularly useful for this task.

Here is an example to illustrate the process:

**Code:**

![Code](https://appinventiv.com/wp-content/uploads/2024/07/how_transformers_outperform_rnns_in_nlp_and_why_it_matters-5.webp)

**Output:**

![Output](https://appinventiv.com/wp-content/uploads/2024/07/how_transformers_outperform_rnns_in_nlp_and_why_it_matters-6.webp)

This output shows each word in the text along with its assigned entity label, such as person (PER), location (LOC), or organization (ORG), demonstrating how Transformers for natural language processing can effectively recognize and classify entities in text.

### Question-Answering (QA)

QA systems use NP with Transformers to provide precise answers to questions based on contextual information. This is essential for search engines, virtual assistants, and educational tools that require accurate and context-aware responses.

Transformer models like BERT, RoBERTa, and T5 are widely used in QA tasks due to their ability to comprehend complex language structures and capture subtle contextual cues. They enable QA systems to accurately respond to inquiries ranging from factual queries to nuanced prompts, enhancing user interaction and information retrieval capabilities in various domains.

### Machine Translation

Transformers have significantly improved machine translation (the task of translating text from one language to another). Models like the original Transformer, T5, and BART can handle this by capturing the nuances and context of languages. They are used in translation services like Google Translate and multilingual communication tools, which we often use to convert text into multiple languages.

Here is an example to illustrate the process:

In the phrase** _ 'She has a keen interest in astronomy,_'** the term 'keen' carries subtle connotations. A standard language model might mistranslate 'keen' as 'intense' (intenso) or 'strong' (fuerte) in Spanish, altering the intended meaning significantly.

A transformer model excels in accurately translating such nuances, ensuring that 'keen' is correctly interpreted as 'gran' in Spanish to convey the precise meaning: **_“Ella tiene un gran interés en la astronomía.”_**

### Conversational AI/Chatbots

Transformers power many advanced [conversational AI](https://appinventiv.com/blog/conversational-ai-how-it-works/) systems and [chatbots](https://appinventiv.com/blog/building-chatbot-using-nlp/), providing natural and engaging responses in dialogue systems. These chatbots leverage machine learning and NLP models trained on extensive datasets containing a wide array of commonly asked questions and corresponding answers. The primary objective of deploying chatbots in business contexts is to promptly address and resolve typical queries. If a query remains unresolved, these chatbots redirect the questions to customer support teams for further assistance.

### Text Generation

Transformers models like GPT-2, GPT-3, and GPT-4 can generate creative human-like text formats, such as scripts, poems, code, musical pieces, emails, and letters. They are highly suitable for creative writing, generating dialogue for conversational agents, and completing text based on a given prompt.

### Text-to-Text Generation

Transformers like T5 and BART can convert one form of text into another, such as paraphrasing, text rewriting, and data-to-text generation. This is useful for tasks like creating different versions of a text, generating summaries, and producing human-readable text from structured data.

You may like reading: **[How to Build an Intelligent AI Model: An Enterprise Guide](https://appinventiv.com/blog/how-to-build-ai-model/)**

## Examples of Transformer NLP Models

When pondering over Transformer vs. RNN, the benefits of transformers far outweigh the traditional NLP models. This is why, in recent years, an increasing number of businesses across industries rely on transformer-based models to perform various NLP tasks, address RNN challenges, and redefine their operations. Here are some of the most notable examples of transformer NLP:

![Life Examples of NLP Transformers](https://appinventiv.com/wp-content/uploads/2024/07/how_transformers_outperform_rnns_in_nlp_and_why_it_matters-9.webp)

### GPT and ChatGPT

OpenAI’s GPT (Generative Pre-trained Transformer) and [ChatGPT ](https://appinventiv.com/blog/chatgpt-integration-in-enterprise/)are advanced NLP models known for their ability to produce coherent and contextually relevant text. GPT-1, the initial model launched in June 2018, set the foundation for subsequent versions. GPT-3, introduced in 2020, represents a significant leap with enhanced capabilities in natural language generation.

These models excel across various domains, including content creation, conversation, language translation, customer support interactions, and even coding assistance.

You may like reading: **[The Cost of Developing a Chatbot like ChatGPT](https://appinventiv.com/blog/cost-to-develop-an-app-like-chatgpt/)**

### BERT

Introduced by Google in 2018, BERT (Bidirectional Encoder Representations from Transformers) is a landmark model in natural language processing. It revolutionized language understanding tasks by leveraging bidirectional training to capture intricate linguistic contexts, enhancing accuracy and performance in complex language understanding tasks.

BERT's versatility extends to various applications such as sentiment analysis, named entity recognition, and question answering.

![NLP Tasks Done by BERT ](https://appinventiv.com/wp-content/uploads/2024/07/how_transformers_outperform_rnns_in_nlp_and_why_it_matters-14.webp)

### LaMDA

Google Introduced a language model, LaMDA (Language Model for Dialogue Applications), in 2021 that aims specifically to enhance dialogue applications and conversational AI systems. This language model represents Google's advancement in natural language understanding and generation technologies.

Unlike RNN, this model is tailored to understand and respond to specific queries and prompts in a conversational context, enhancing user interactions in various applications.

### T5

T5 (Text-To-Text Transfer Transformer) is another versatile model designed by Google AI in 2019. It is known for framing all NLP tasks as text-to-text problems, which means that both the inputs and outputs are text-based. This approach allows T5 to handle diverse functions like translation, summarization, and classification seamlessly.

Deployed in Google Translate and other applications, T5 is most prominently used in the retail and eCommerce industry to generate high-quality translations, concise summaries, reviews, and product descriptions.

![NLP Task Done by T5](https://appinventiv.com/wp-content/uploads/2024/07/how_transformers_outperform_rnns_in_nlp_and_why_it_matters-10.webp)

## Future of NLP Transformers - Redefining the AI Era

In the pursuit of RNN vs. Transformer, the latter has truly won the trust of technologists, continuously pushing the boundaries of what is possible and revolutionizing the AI era. While currently used for regular NLP tasks (mentioned above), researchers are discovering new applications every day.

Accordingly, the future of Transformers looks bright, with ongoing research aimed at enhancing their efficiency and scalability, paving the way for more versatile and accessible applications.

For instance, the ever-increasing advancements in popular transformer models such as Google’s PaLM 2 or OpenAI’s GPT-4 indicate that the use of transformers in NLP will continue to rise in the coming years.

![ Usage of some popular NLP transformers over time](https://appinventiv.com/wp-content/uploads/2024/07/how_transformers_outperform_rnns_in_nlp_and_why_it_matters-11.webp)

Advances in NLP with Transformers facilitate their deployment in real-time applications such as live translation, transcription, and sentiment analysis. Additionally, integrating Transformers with multiple data types—text, images, and audio—will enhance their capability to perform complex multimodal tasks.

Transformers will also see increased use in domain-specific applications, improving accuracy and relevance in fields like healthcare, finance, and legal services. Furthermore, efforts to address ethical concerns, break down language barriers, and mitigate biases will enhance the accessibility and reliability of these models, facilitating more inclusive global communication.

In straight terms, research is a driving force behind the rapid advancements in NLP Transformers, unveiling revolutionary use cases at an unprecedented pace and shaping the future of these models. These ongoing advancements in NLP with Transformers across various sectors will redefine how we interact with and [benefit from artificial intelligence](https://appinventiv.com/blog/ai-impact-on-business/).

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## Enter into the Golden Era of NLP with Appinventiv

As businesses strive to adopt the latest in AI technology, choosing between Transformer and RNN models is a crucial decision. In the ongoing evolution of NLP and AI, Transformers have clearly outpaced RNNs in performance and efficiency.

Their ability to handle parallel processing, understand long-range dependencies, and manage vast datasets makes them superior for a wide range of NLP tasks. From language translation to conversational AI, the benefits of Transformers are evident, and their impact on businesses across industries is profound.

Interested in integrating these cutting-edge models into your operations? Let’s dive into this exciting adventure with Appinventiv. As a leading [AI development company](https://appinventiv.com/ai-development-services/), we excel at developing and deploying Transformer-based solutions, enabling businesses to enhance their AI initiatives and take their businesses to the next level.

[Gurushala](https://appinventiv.com/portfolio/gurushala-online-learning-platform/), [Mudra](https://appinventiv.com/portfolio/mudra-budget-management-app/), Tootle, [JobGet](https://appinventiv.com/portfolio/jobget-job-search-app/), Chat & More, HouseEazy, [YouComm](https://appinventiv.com/portfolio/youcomm-health-app/), and [Vyrb ](https://appinventiv.com/portfolio/vyrb-social-media-app/)are some of our esteemed clients who have leveraged our AI capabilities to build next-gen applications and unlock new possibilities in today’s tech world.

![Chat & More: Sticker/Image generation feature developed by Appinventiv](https://appinventiv.com/wp-content/uploads/2024/07/how_transformers_outperform_rnns_in_nlp_and_why_it_matters-12.webp)

Still unsure about transformer vs. recurrent neural network capability?[ Get in touch with us](https://appinventiv.com/contact/) to uncover more and learn how you can leverage transformers for natural language processing in your organization.

## FAQs

**Q. What are transformers in NLP?**

**A.** Transformers in NLP are a type of deep learning model specifically designed to handle sequential data. They use self-attention mechanisms to weigh the significance of different words in a sentence, allowing them to capture relationships and dependencies without sequential processing like in traditional RNNs.

**Q. How is transformer different from RNN with attention?**

**A.** Transformers and RNNs both handle sequential data but differ in their approach, efficiency, performance, and many other aspects. For instance, Transformers utilize a self-attention mechanism to evaluate the significance of every word in a sentence simultaneously, which lets them handle longer sequences more efficiently.

This "looking at everything at once" approach means transformers are more parallelizable than RNNs, which process data sequentially. This parallel processing capability gives natural language processing with Transformers a computational advantage and allows them to capture global dependencies effectively.

Thus, when comparing RNN vs. Transformer, we can say that RNNs are effective for some sequential tasks, while transformers excel in tasks requiring a comprehensive understanding of contextual relationships across entire sequences.

**Q. What is the advantage of transformers NLP?**

**A.** There are many advantages of using transformers NLP, such as:

  * Transformers excel at capturing relationships between words across long distances, which is crucial for understanding context in NLP tasks.
  * They can process and analyze input data in parallel, making them faster and more efficient than sequential models like RNNs.
  * The self-attention mechanisms of Transformer architecture NLP enable them to scale up efficiently, handling large datasets and complex tasks without compromising performance.
  * Pre-trained transformer models (like BERT and GPT) can be fine-tuned for specific tasks with relatively small amounts of task-specific data, offering improved performance and faster deployment.
  * Natural language processing with transformers has achieved state-of-the-art results in various NLP benchmarks. It demonstrates their effectiveness in tasks such as language translation, text classification, and summarization.



These game-changing benefits of transformers make businesses go with the former option when evaluating – Transformer vs RNN.

![](https://appinventiv.com/wp-content/uploads/2019/12/chirag-150x150.png)

**THE AUTHOR**

Chirag Bharadwaj

AVP - Technology

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Nearly 92% of companies invest in AI, but only 1% of leaders and CEOs consider themselves mature in deploying AI in their operations. This is a huge gap—who is responsible? Leaders and CEOs still treat AI as an experimental project. For years, 92% of businesses that are using AI have always associated the adoption of…

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Technology is moving faster than ever, and businesses are racing to keep up! It’s no longer enough to stick to old-school methods. Companies must jump on the tech bandwagon and let smart innovations power their operations to stay ahead. Ever wondered how stores seem to know when shelves need restocking or how checkout lines magically…

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# Rnn Vs Lstm Vs Transformer Models

Last updated on 03/10/25

Explore the differences between RNN, LSTM, and Transformer models in deep learning for better performance and efficiency.

On this page

  * RNNs: Limitations in Sequence Modeling
  * LSTMs: Enhancements Over RNNs
  * Transformers: A Paradigm Shift
  * Understanding Transformer Models: A Deep Dive



Sources

[![favicon](/_next/image?url=https%3A%2F%2Ficons.duckduckgo.com%2Fip2%2Farxiv.org.ico&w=32&q=75)arxiv.orgA Review of Hybrid and Ensemble in Deep Learning for Natural Language Processing](http://arxiv.org/pdf/2312.05589v2 "A Review of Hybrid and Ensemble in Deep Learning for Natural Language
  Processing")[![favicon](/_next/image?url=https%3A%2F%2Ficons.duckduckgo.com%2Fip2%2Fdeeprevision.github.io.ico&w=32&q=75)deeprevision.github.ioThe Transformer Blueprint: A Holistic Guide to the Transformer Neural Network Architecture](https://deeprevision.github.io/posts/001-transformer/ "The Transformer Blueprint: A Holistic Guide to the Transformer Neural Network Architecture")[![favicon](/_next/image?url=https%3A%2F%2Ficons.duckduckgo.com%2Fip2%2Farxiv.org.ico&w=32&q=75)arxiv.orgLearning from Demonstration with Implicit Nonlinear Dynamics Models](http://arxiv.org/pdf/2409.18768v1 "Learning from Demonstration with Implicit Nonlinear Dynamics Models")

RNNs: Limitations in Sequence Modeling

Recurrent Neural Networks (RNNs) are designed to process sequential data by maintaining a hidden state that captures information from previous inputs. This design allows RNNs to effectively model temporal dynamics, making them suitable for tasks such as sentiment analysis and language translation. However, RNNs face significant limitations that can hinder their performance, particularly when dealing with long sequences.

## Core Limitations of RNNs

  * **Vanishing Gradient Problem** : One of the most critical challenges faced by RNNs is the vanishing gradient problem. During training, gradients can diminish exponentially as they are propagated back through time, making it difficult for the network to learn long-range dependencies. This issue is particularly pronounced in standard RNN architectures, which struggle to retain information over extended sequences.

  * **Computational Demand** : RNNs require substantial computational resources, especially when processing long sequences. The sequential nature of RNNs means that each input must be processed one at a time, leading to increased training times and resource consumption. The Backpropagation Through Time (BPTT) algorithm, while effective, further exacerbates this issue by requiring the network to be unrolled over time, which can be computationally expensive.

  * **Scalability Issues** : RNNs are inherently difficult to parallelize due to their sequential processing requirements. This limitation restricts their scalability on modern machine learning hardware, making it challenging to train large models efficiently. As a result, RNNs may not be the best choice for applications requiring rapid processing of extensive datasets.




## Comparison with LSTMs and GRUs

To address some of the limitations of standard RNNs, advanced architectures such as Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs) have been developed. These architectures incorporate mechanisms to better manage the flow of information and mitigate the vanishing gradient problem:

  * **LSTMs** : LSTMs introduce a cell state and gating mechanisms that allow the network to retain information over longer periods. This design enables LSTMs to perform better than standard RNNs in tasks requiring the understanding of long-range dependencies.

  * **GRUs** : GRUs are a simplified version of LSTMs that combine the forget and input gates into a single update gate. While they are generally more efficient than LSTMs, they still share some of the same limitations, particularly regarding scalability and the ability to model global context effectively.




## Conclusion

In summary, while RNNs are powerful tools for sequence modeling, they come with inherent limitations that can impact their effectiveness in certain applications. The introduction of LSTMs and GRUs has provided some solutions, but challenges such as computational demand and scalability remain significant hurdles. As the field of deep learning continues to evolve, exploring alternative architectures like Transformers may offer more robust solutions for handling complex sequential data.

### Related answers

  * [Difference Between Transformer And LstmExplore the key differences between Transformer and LSTM models in deep learning, focusing on architecture and performance.](/p/transformer-models-difference-answer-cat-ai#cm1qtova00gfnzxecskphwad4)
  * [Transformer Vs Llm ComparisonExplore the differences between transformer models and LLMs, focusing on architecture, performance, and applications.](/p/transformer-models-answer-transformer-vs-llm-cat-ai#cm1qur0x00op9zxecdkrm8xiy)
  * [Transformer Google Paper InsightsExplore the key findings and implications of the Transformer Google paper, enhancing your understanding of Transformer models.](/p/transformer-models-answer-transformer-google-paper-cat-ai#cm1qurh1o0ou9zxec0agfwov9)



BuildReplayFunctions

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![Documentation illustration](/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fev3amoz3%2Fproduction%2F8ad4f7a31cf46d4f64f6bc9e081edfd591c7bb87-800x800.png&w=1920&q=75)

Sources

[![favicon](/_next/image?url=https%3A%2F%2Ficons.duckduckgo.com%2Fip2%2Farxiv.org.ico&w=32&q=75)arxiv.orgAn Efficient Consolidation of Word Embedding and Deep Learning Techniques for Classifying Anticancer Peptides: FastText+BiLSTM](http://arxiv.org/pdf/2309.12058v1 "An Efficient Consolidation of Word Embedding and Deep Learning
  Techniques for Classifying Anticancer Peptides: FastText+BiLSTM")[![favicon](/_next/image?url=https%3A%2F%2Ficons.duckduckgo.com%2Fip2%2Farxiv.org.ico&w=32&q=75)arxiv.orgA Review of Hybrid and Ensemble in Deep Learning for Natural Language Processing](http://arxiv.org/pdf/2312.05589v2 "A Review of Hybrid and Ensemble in Deep Learning for Natural Language
  Processing")[![favicon](/_next/image?url=https%3A%2F%2Ficons.duckduckgo.com%2Fip2%2Farxiv.org.ico&w=32&q=75)arxiv.orgComprehensive Study on Sentiment Analysis: From Rule-based to modern LLM based system](http://arxiv.org/pdf/2409.09989v1 "Comprehensive Study on Sentiment Analysis: From Rule-based to modern LLM
  based system")

LSTMs: Enhancements Over RNNs

Long Short-Term Memory (LSTM) networks are a specialized variant of Recurrent Neural Networks (RNNs) that address the limitations of traditional RNNs in capturing long-term dependencies in sequential data. Introduced by Hochreiter and Schmidhuber, LSTMs incorporate memory cells that allow for selective retention or forgetting of information over extended sequences, which is crucial for tasks requiring context retention.

## Key Components of LSTMs

LSTMs consist of three main components that regulate the flow of information:

  * **Input Gate** : Determines which values to update in the memory cell.
  * **Forget Gate** : Decides what information to discard from the memory cell.
  * **Output Gate** : Controls what information is outputted from the memory cell to the next layer.



These gates work together to manage the information flow, enabling LSTMs to learn from sequences effectively. The architecture of an LSTM unit is designed to combat the vanishing gradient problem, which is a common issue in traditional RNNs that leads to the loss of information over long sequences.

## Applications of LSTMs

LSTMs have been successfully applied in various domains, including:

  * **Natural Language Processing (NLP)** : For tasks such as sentiment analysis and machine translation.
  * **Speech Recognition** : To improve the accuracy of transcribing spoken language.
  * **Time Series Analysis** : For predicting future values based on historical data.
  * **Music Generation** : To create compositions that follow learned patterns.



## Performance Factors

The effectiveness of LSTM models is influenced by several factors:

  * **Network Architecture** : The number of layers and units can significantly impact performance.
  * **Training Data Quality** : High-quality, diverse datasets lead to better model generalization.
  * **Optimization Algorithms** : The choice of algorithm can affect convergence speed and model accuracy.
  * **Hyperparameters** : Tuning parameters such as learning rate and batch size is crucial for optimal performance.



## LSTMs vs RNNs vs Transformers

When comparing LSTMs to traditional RNNs and Transformers, it is essential to note:

  * **LSTMs** excel in capturing long-range dependencies due to their gating mechanisms.
  * **RNNs** struggle with long sequences due to the vanishing gradient problem.
  * **Transformers** , while powerful, rely on self-attention mechanisms and may require more computational resources.



In conclusion, LSTMs represent a significant advancement over traditional RNNs, particularly in their ability to handle complex sequential data and maintain contextual information. Their integration with other architectures, such as Convolutional Neural Networks (CNNs), further enhances their capabilities in various applications.

### Related answers

  * [Transformer Vs Llm ComparisonExplore the differences between transformer models and LLMs, focusing on architecture, performance, and applications.](/p/transformer-models-answer-transformer-vs-llm-cat-ai#cm1qxxfdh1fc3zxec2s8n2eta)
  * [Transformer Models Rnn OverviewExplore the intricacies of RNNs in transformer models, their architecture, and applications in deep learning.](/p/transformer-models-knowledge-rnn-cat-ai#cm0oyho2u0no6alqf5eeahke3)
  * [Transformer Vs Neural Network ComparisonExplore the differences between transformer models and neural networks, focusing on architecture, performance, and applications.](/p/transformer-models-answer-transformer-vs-neural-network-cat-ai#cm1qry96h0269zxecanjc8ujb)



Sources

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[![favicon](/_next/image?url=https%3A%2F%2Ficons.duckduckgo.com%2Fip2%2Farxiv.org.ico&w=32&q=75)arxiv.orgNeural Natural Language Processing for Long Texts: A Survey on Classification and Summarization](http://arxiv.org/pdf/2305.16259v6 "Neural Natural Language Processing for Long Texts: A Survey on
  Classification and Summarization")[![favicon](/_next/image?url=https%3A%2F%2Ficons.duckduckgo.com%2Fip2%2Farxiv.org.ico&w=32&q=75)arxiv.orgMechanistic interpretability of large language models with applications to the financial services industry](http://arxiv.org/pdf/2407.11215v1 "Mechanistic interpretability of large language models with applications
  to the financial services industry")[![favicon](/_next/image?url=https%3A%2F%2Ficons.duckduckgo.com%2Fip2%2Farxiv.org.ico&w=32&q=75)arxiv.orgNatural Language Processing Methods for Symbolic Music Generation and Information Retrieval: a Survey](http://arxiv.org/pdf/2402.17467v1 "Natural Language Processing Methods for Symbolic Music Generation and
  Information Retrieval: a Survey")

Transformers: A Paradigm Shift

Transformers have revolutionized the field of natural language processing (NLP) and sequence modeling, offering significant advantages over traditional recurrent neural networks (RNNs) and long short-term memory (LSTM) models. Unlike RNNs, which process data sequentially and rely on an internal hidden state, transformers utilize an attention mechanism that allows for parallel processing of data. This capability enables transformers to capture long-term dependencies more effectively, making them superior for various NLP tasks.

## Attention Mechanism

The attention mechanism is at the heart of the transformer architecture. It allows the model to weigh the importance of different words in a sequence when making predictions. This is particularly useful in tasks such as machine translation, where understanding the context of a word in relation to others is crucial. The attention types can be categorized as:

  * **Bidirectional/Unmasked Attention** : Used in tasks where the entire context is available.
  * **Unidirectional/Masked Attention** : Employed in tasks like language modeling where future tokens should not influence the prediction of the current token.



## Transformer Architecture

The transformer architecture consists of two main components: the encoder and the decoder. The encoder processes the input data and maps it into a latent space, while the decoder takes this latent representation to generate the output. This separation allows for more flexibility and efficiency in handling various tasks, such as:

  * **Sequence-to-Sequence Tasks** : Like machine translation, where the model learns to convert one sequence into another.
  * **Text Classification** : Such as sentiment analysis, where the model predicts a label for a given text.



## Advancements in Transformer Models

Recent advancements in transformer architecture have led to the development of large language models (LLMs) like BERT and GPT-4. These models leverage transfer learning, allowing them to be pre-trained on vast datasets and fine-tuned for specific tasks. This approach not only enhances performance but also reduces the time and resources needed for training new models.

## Challenges and Future Directions

Despite their success, transformers face challenges, including the need for more efficient models and effective utilization of context windows. Addressing these challenges presents exciting opportunities for future research. As the field of deep learning continues to evolve, the transformer architecture remains a foundational element, paving the way for innovative applications beyond NLP.

In summary, the transformer model's ability to process data in parallel and capture complex dependencies has made it a cornerstone of modern machine learning systems. As we look to the future, the evolution of transformer architectures will undoubtedly continue to shape the landscape of AI and machine learning.

### Related answers

  * [Transformer Vs Attention ModelsExplore the differences between transformer and attention models, their architectures, and applications in deep learning.](/p/transformer-models-answer-transformer-vs-attention-cat-ai#cm1qxxmzf1ff1zxecslo65gy7)
  * [Transformer Models ExamplesExplore various examples of transformer models and their applications in natural language processing and machine learning.](/p/transformer-models-answer-examples-cat-ai#cm1qrxpoj01ynzxecj3q6gove)
  * [Transformer Models Introduction CatalogExplore the fundamentals and catalog of transformer models, their architecture, and applications in modern AI.](/p/transformer-models-answer-introduction-catalog-cat-ai#cm0owrwn50ltmalqffy1iyov0)



BuildReplayFunctions

Restack AI SDK

The framework for AI agents

Build reliable and accurate AI agents in code, capable of running and persisting month-lasting processes in the background.

[Learn more ->](https://docs.restack.io)

![Documentation illustration](/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fev3amoz3%2Fproduction%2F8ad4f7a31cf46d4f64f6bc9e081edfd591c7bb87-800x800.png&w=1920&q=75)

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[![favicon](/_next/image?url=https%3A%2F%2Ficons.duckduckgo.com%2Fip2%2Fdatasciencelearningcenter.substack.com.ico&w=32&q=75)datasciencelearningcenter.substack.comHow do Transformers Work in NLP? A Guide to the Latest State ... - Substack](https://datasciencelearningcenter.substack.com/p/how-do-transformers-work-in-nlp-a "How do Transformers Work in NLP? A Guide to the Latest State ... - Substack")[![favicon](/_next/image?url=https%3A%2F%2Ficons.duckduckgo.com%2Fip2%2Farxiv.org.ico&w=32&q=75)arxiv.orgMechanistic interpretability of large language models with applications to the financial services industry](http://arxiv.org/pdf/2407.11215v1 "Mechanistic interpretability of large language models with applications
  to the financial services industry")[![favicon](/_next/image?url=https%3A%2F%2Ficons.duckduckgo.com%2Fip2%2Farxiv.org.ico&w=32&q=75)arxiv.orgEfficiency optimization of large-scale language models based on deep learning in natural language processing tasks](http://arxiv.org/pdf/2405.11704v1 "Efficiency optimization of large-scale language models based on deep
  learning in natural language processing tasks")

Understanding Transformer Models: A Deep Dive

Transformers have revolutionized the landscape of artificial intelligence (AI) and machine learning (ML), particularly in natural language processing (NLP). Their architecture allows for efficient handling of sequential data, making them a preferred choice for various applications. This section delves into the intricacies of transformer models, their components, and their applications in modern AI systems.

## Core Components of Transformer Models

Transformers consist of several key components that work together to process data effectively:

  * **Self-Attention Mechanism** : This mechanism allows the model to weigh the importance of different words in a sentence, enabling it to capture contextual relationships. It computes attention scores for each word relative to others, facilitating a deeper understanding of the input data.

  * **Multi-Head Attention** : By employing multiple attention heads, transformers can focus on different parts of the input simultaneously. This parallel processing enhances the model's ability to capture diverse linguistic features.

  * **Positional Encoding** : Since transformers do not process data sequentially, positional encodings are added to input embeddings to retain the order of words. This is crucial for understanding the structure of sentences.

  * **Feed-Forward Neural Networks** : After the attention mechanism, the output is passed through feed-forward networks, which apply non-linear transformations to enhance the model's expressiveness.

  * **Layer Normalization and Residual Connections** : These techniques help stabilize training and improve convergence by allowing gradients to flow more easily through the network.




## Differences Between Attention Models and Transformers

While attention mechanisms can be used independently, transformers integrate them into a comprehensive architecture. Key differences include:

  * **Architecture** : Attention models may focus on specific tasks, whereas transformers provide a versatile framework applicable to various NLP tasks.
  * **Scalability** : Transformers are designed to scale efficiently with larger datasets and more complex tasks, making them suitable for large language models (LLMs).



## Applications of Transformers in AI and ML

Transformers have found applications across multiple domains, including:

  * **Natural Language Processing** : Tasks such as machine translation, text summarization, and sentiment analysis benefit significantly from transformer architectures.
  * **Computer Vision** : Recent advancements have seen transformers applied to image processing tasks, demonstrating their versatility beyond text.
  * **Reinforcement Learning** : Transformers are being explored in reinforcement learning scenarios, where they can model complex environments and decision-making processes.



## Conclusion

The transformer architecture has set a new standard in AI and ML, enabling breakthroughs in various fields. As research continues, we can expect further innovations that leverage the strengths of transformers, paving the way for more efficient and capable AI systems.

### Related answers

  * [Transformer Models for Sentiment AnalysisExplore how transformer models enhance sentiment analysis, improving accuracy and understanding of textual data.](/p/transformer-models-answer-sentiment-analysis-cat-ai#cm0oyafk50nk5alqftysd702m)
  * [Transformer Models Study InsightsExplore the intricacies of transformer models, their architecture, and applications in modern AI research.](/p/transformer-models-answer-study-cat-ai#cm0ox05v70m73alqfxhmphtrz)
  * [Transformer Models for Text AnalysisExplore NLP transformer models designed for efficient text analysis, enhancing understanding and processing of natural language data.](/p/transformer-models-answer-text-analysis-cat-ai#cm1wqzokw4ib6ujmetxxghcsu)



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url: http://arxiv.org/abs/1702.01923v1
title: [1702.01923v1] Comparative Study of CNN and RNN for Natural Language Processing
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# Computer Science > Computation and Language

**arXiv:1702.01923v1** (cs) 

[Submitted on 7 Feb 2017]

# Title:Comparative Study of CNN and RNN for Natural Language Processing

Authors:[Wenpeng Yin](https://arxiv.org/search/cs?searchtype=author&query=Yin,+W), [Katharina Kann](https://arxiv.org/search/cs?searchtype=author&query=Kann,+K), [Mo Yu](https://arxiv.org/search/cs?searchtype=author&query=Yu,+M), [Hinrich Schütze](https://arxiv.org/search/cs?searchtype=author&query=Sch%C3%BCtze,+H)

View a PDF of the paper titled Comparative Study of CNN and RNN for Natural Language Processing, by Wenpeng Yin and 3 other authors

[View PDF](/pdf/1702.01923v1)

> Abstract:Deep neural networks (DNN) have revolutionized the field of natural language processing (NLP). Convolutional neural network (CNN) and recurrent neural network (RNN), the two main types of DNN architectures, are widely explored to handle various NLP tasks. CNN is supposed to be good at extracting position-invariant features and RNN at modeling units in sequence. The state of the art on many NLP tasks often switches due to the battle between CNNs and RNNs. This work is the first systematic comparison of CNN and RNN on a wide range of representative NLP tasks, aiming to give basic guidance for DNN selection. 

Comments: | 7 pages, 11 figures  
---|---  
Subjects: |  Computation and Language (cs.CL)  
Cite as: | [arXiv:1702.01923](https://arxiv.org/abs/1702.01923) [cs.CL]  
  | (or  [arXiv:1702.01923v1](https://arxiv.org/abs/1702.01923v1) [cs.CL] for this version)   
  |  <https://doi.org/10.48550/arXiv.1702.01923> Focus to learn more arXiv-issued DOI via DataCite  
  
## Submission history

From: Wenpeng Yin [[view email](/show-email/6147fb6e/1702.01923)]   
**[v1]** Tue, 7 Feb 2017 08:33:35 UTC (88 KB)  


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url: http://arxiv.org/abs/2307.06540v1
scrape_date: 2025-03-12T11:17:33.193306
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[2307.06540v1] Convolutional Neural Networks for Sentiment Analysis on Weibo Data: A Natural Language Processing Approach

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# Computer Science > Computation and Language

**arXiv:2307.06540v1** (cs) 

[Submitted on 13 Jul 2023]

# Title:Convolutional Neural Networks for Sentiment Analysis on Weibo Data: A Natural Language Processing Approach

Authors:[Yufei Xie](https://arxiv.org/search/cs?searchtype=author&query=Xie,+Y), [Rodolfo C. Raga Jr](https://arxiv.org/search/cs?searchtype=author&query=Raga,+R+C)

View a PDF of the paper titled Convolutional Neural Networks for Sentiment Analysis on Weibo Data: A Natural Language Processing Approach, by Yufei Xie and Rodolfo C. Raga Jr

[View PDF](/pdf/2307.06540v1)

> Abstract:This study addressed the complex task of sentiment analysis on a dataset of 119,988 original tweets from Weibo using a Convolutional Neural Network (CNN), offering a new approach to Natural Language Processing (NLP). The data, sourced from Baidu's PaddlePaddle AI platform, were meticulously preprocessed, tokenized, and categorized based on sentiment labels. A CNN-based model was utilized, leveraging word embeddings for feature extraction, and trained to perform sentiment classification. The model achieved a macro-average F1-score of approximately 0.73 on the test set, showing balanced performance across positive, neutral, and negative sentiments. The findings underscore the effectiveness of CNNs for sentiment analysis tasks, with implications for practical applications in social media analysis, market research, and policy studies. The complete experimental content and code have been made publicly available on the Kaggle data platform for further research and development. Future work may involve exploring different architectures, such as Recurrent Neural Networks (RNN) or transformers, or using more complex pre-trained models like BERT, to further improve the model's ability to understand linguistic nuances and context. 

Subjects: |  Computation and Language (cs.CL); Machine Learning (cs.LG)  
---|---  
ACM classes: | I.2.7  
Cite as: | [arXiv:2307.06540](https://arxiv.org/abs/2307.06540) [cs.CL]  
  | (or  [arXiv:2307.06540v1](https://arxiv.org/abs/2307.06540v1) [cs.CL] for this version)   
  |  <https://doi.org/10.48550/arXiv.2307.06540> Focus to learn more arXiv-issued DOI via DataCite  
  
## Submission history

From: Yufei Xie [[view email](/show-email/0997bf5a/2307.06540)]   
**[v1]** Thu, 13 Jul 2023 03:02:56 UTC (311 KB)  


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url: http://arxiv.org/abs/2312.05589v2
title: [2312.05589v2] A Review of Hybrid and Ensemble in Deep Learning for Natural Language Processing
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# Computer Science > Artificial Intelligence

**arXiv:2312.05589v2** (cs) 

[Submitted on 9 Dec 2023 ([v1](https://arxiv.org/abs/2312.05589v1)), last revised 8 Aug 2024 (this version, v2)]

# Title:A Review of Hybrid and Ensemble in Deep Learning for Natural Language Processing

Authors:[Jianguo Jia](https://arxiv.org/search/cs?searchtype=author&query=Jia,+J), [Wen Liang](https://arxiv.org/search/cs?searchtype=author&query=Liang,+W), [Youzhi Liang](https://arxiv.org/search/cs?searchtype=author&query=Liang,+Y)

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> Abstract:This review presents a comprehensive exploration of hybrid and ensemble deep learning models within Natural Language Processing (NLP), shedding light on their transformative potential across diverse tasks such as Sentiment Analysis, Named Entity Recognition, Machine Translation, Question Answering, Text Classification, Generation, Speech Recognition, Summarization, and Language Modeling. The paper systematically introduces each task, delineates key architectures from Recurrent Neural Networks (RNNs) to Transformer-based models like BERT, and evaluates their performance, challenges, and computational demands. The adaptability of ensemble techniques is emphasized, highlighting their capacity to enhance various NLP applications. Challenges in implementation, including computational overhead, overfitting, and model interpretation complexities, are addressed alongside the trade-off between interpretability and performance. Serving as a concise yet invaluable guide, this review synthesizes insights into tasks, architectures, and challenges, offering a holistic perspective for researchers and practitioners aiming to advance language-driven applications through ensemble deep learning in NLP. 

Subjects: |  Artificial Intelligence (cs.AI)  
---|---  
Cite as: | [arXiv:2312.05589](https://arxiv.org/abs/2312.05589) [cs.AI]  
  | (or  [arXiv:2312.05589v2](https://arxiv.org/abs/2312.05589v2) [cs.AI] for this version)   
  |  <https://doi.org/10.48550/arXiv.2312.05589> Focus to learn more arXiv-issued DOI via DataCite  
  
## Submission history

From: Jianguo Jia [[view email](/show-email/41460f90/2312.05589)]   
**[[v1]](/abs/2312.05589v1)** Sat, 9 Dec 2023 14:49:34 UTC (2,762 KB)  
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url: http://arxiv.org/abs/2205.02724v1
title: [2205.02724v1] Implicit N-grams Induced by Recurrence
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# Computer Science > Computation and Language

**arXiv:2205.02724v1** (cs) 

[Submitted on 5 May 2022]

# Title:Implicit N-grams Induced by Recurrence

Authors:[Xiaobing Sun](https://arxiv.org/search/cs?searchtype=author&query=Sun,+X), [Wei Lu](https://arxiv.org/search/cs?searchtype=author&query=Lu,+W)

View a PDF of the paper titled Implicit N-grams Induced by Recurrence, by Xiaobing Sun and Wei Lu

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> Abstract:Although self-attention based models such as Transformers have achieved remarkable successes on natural language processing (NLP) tasks, recent studies reveal that they have limitations on modeling sequential transformations (Hahn, 2020), which may prompt re-examinations of recurrent neural networks (RNNs) that demonstrated impressive results on handling sequential data. Despite many prior attempts to interpret RNNs, their internal mechanisms have not been fully understood, and the question on how exactly they capture sequential features remains largely unclear. In this work, we present a study that shows there actually exist some explainable components that reside within the hidden states, which are reminiscent of the classical n-grams features. We evaluated such extracted explainable features from trained RNNs on downstream sentiment analysis tasks and found they could be used to model interesting linguistic phenomena such as negation and intensification. Furthermore, we examined the efficacy of using such n-gram components alone as encoders on tasks such as sentiment analysis and language modeling, revealing they could be playing important roles in contributing to the overall performance of RNNs. We hope our findings could add interpretability to RNN architectures, and also provide inspirations for proposing new architectures for sequential data. 

Comments: | 16 pages, 7 figures, accepted by the NAACL 2022 main conference  
---|---  
Subjects: |  Computation and Language (cs.CL)  
Cite as: | [arXiv:2205.02724](https://arxiv.org/abs/2205.02724) [cs.CL]  
  | (or  [arXiv:2205.02724v1](https://arxiv.org/abs/2205.02724v1) [cs.CL] for this version)   
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## Submission history

From: Xiaobing Sun [[view email](/show-email/5e6592a7/2205.02724)]   
**[v1]** Thu, 5 May 2022 15:53:46 UTC (1,762 KB)  


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url: http://arxiv.org/abs/1610.04841v2
title: [1610.04841v2] Translation Quality Estimation using Recurrent Neural Network
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# Computer Science > Computation and Language

**arXiv:1610.04841v2** (cs) 

[Submitted on 16 Oct 2016 ([v1](https://arxiv.org/abs/1610.04841v1)), last revised 21 Oct 2016 (this version, v2)]

# Title:Translation Quality Estimation using Recurrent Neural Network

Authors:[Raj Nath Patel](https://arxiv.org/search/cs?searchtype=author&query=Patel,+R+N), [Sasikumar M](https://arxiv.org/search/cs?searchtype=author&query=M,+S)

View a PDF of the paper titled Translation Quality Estimation using Recurrent Neural Network, by Raj Nath Patel and Sasikumar M

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> Abstract:This paper describes our submission to the shared task on word/phrase level Quality Estimation (QE) in the First Conference on Statistical Machine Translation (WMT16). The objective of the shared task was to predict if the given word/phrase is a correct/incorrect (OK/BAD) translation in the given sentence. In this paper, we propose a novel approach for word level Quality Estimation using Recurrent Neural Network Language Model (RNN-LM) architecture. RNN-LMs have been found very effective in different Natural Language Processing (NLP) applications. RNN-LM is mainly used for vector space language modeling for different NLP problems. For this task, we modify the architecture of RNN-LM. The modified system predicts a label (OK/BAD) in the slot rather than predicting the word. The input to the system is a word sequence, similar to the standard RNN-LM. The approach is language independent and requires only the translated text for QE. To estimate the phrase level quality, we use the output of the word level QE system. 

Comments: | 7 pages, published at First Conference on Machine Translation  
---|---  
Subjects: |  Computation and Language (cs.CL)  
Cite as: | [arXiv:1610.04841](https://arxiv.org/abs/1610.04841) [cs.CL]  
  | (or  [arXiv:1610.04841v2](https://arxiv.org/abs/1610.04841v2) [cs.CL] for this version)   
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## Submission history

From: Raj Nath Patel [[view email](/show-email/b89476a2/1610.04841)]   
**[[v1]](/abs/1610.04841v1)** Sun, 16 Oct 2016 10:54:23 UTC (23 KB)  
**[v2]** Fri, 21 Oct 2016 07:01:05 UTC (23 KB)  


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title: How Transformer changed NLP and machine learning | Ganesh Jagadeesan posted on the topic | LinkedIn
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Absolutely stellar overview of "Attention Is All You Need"! This groundbreaking paper by Vaswani et al. fundamentally changed the landscape of NLP and machine learning. The introduction of the Transformer model has paved the way for revolutionary advancements in the field, enabling the creation of powerful models like BERT, GPT, and T5. The detailed breakdown of the key concepts like self-attention, multi-head attention, and positional encoding highlights the ingenuity behind the Transformer architecture. It's exciting to see how this model continues to impact tasks like machine translation, text summarization, and more. Kudos to the team for their transformative work! Thank you for sharing this. 🧠📚✨ [#NLP](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Fnlp&trk=public_post-text) [#MachineLearning](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Fmachinelearning&trk=public_post-text) [#Transformers](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Ftransformers&trk=public_post-text) [#AI](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Fai&trk=public_post-text) [#DeepLearning](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Fdeeplearning&trk=public_post-text) [#Innovation](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Finnovation&trk=public_post-text) [#BERT](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Fbert&trk=public_post-text) [#GPT](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Fgpt&trk=public_post-text) [#TextGeneration](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Ftextgeneration&trk=public_post-text) [#AIResearch](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Fairesearch&trk=public_post-text) 🚀🔍

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"Attention Is All You Need" is a seminal paper in the field of natural language processing and machine learning, authored by Vaswani et al. in 2017. This paper introduced the Transformer model, which has since become a cornerstone in the development of advanced neural networks, particularly for tasks involving sequence data like language translation and text generation. Here's a brief overview of the key concepts: Key Concepts of "Attention Is All You Need" 1\. Transformer Architecture: \- The Transformer model replaces traditional sequence-based models like RNNs and LSTMs with a more efficient architecture based solely on self-attention mechanisms. \- It consists of an encoder and a decoder, each made up of multiple layers of self-attention and feedforward neural networks. 2\. Self-Attention Mechanism: \- Self-attention allows the model to weigh the importance of different words in a sequence relative to each other. \- It calculates three vectors for each word: Query (Q), Key (K), and Value (V). The attention score is computed by taking the dot product of Q and K, which is then used to weigh the V vectors. 3\. Multi-Head Attention: \- Instead of performing a single attention operation, the model uses multiple attention mechanisms (heads) in parallel. Each head focuses on different parts of the input sequence, capturing various aspects of the relationships between words. \- The outputs of these heads are concatenated and linearly transformed to produce the final attention output. 4\. Positional Encoding: \- Since Transformers do not have a built-in notion of the order of words, positional encodings are added to the input embeddings to provide information about the position of each word in the sequence. 5\. Feedforward Neural Networks: \- Each layer in the encoder and decoder has a feedforward neural network that processes the attention outputs. These networks are identical across layers but have different parameters. 6\. Residual Connections and Layer Normalization: \- Residual connections (additive connections) are used around each sub-layer (self-attention and feedforward networks) to help with the flow of gradients during training. \- Layer normalization is applied after each sub-layer to stabilize and accelerate the training process. Impact and Applications The Transformer model has revolutionized many NLP tasks, leading to the development of models like BERT, GPT, and T5. Its ability to handle long-range dependencies and parallelize training has made it a preferred choice for tasks such as: \- Machine Translation \- Text Summarization \- Question Answering \- Text Generation Conclusion The introduction of the Transformer model marked a significant advancement in the field of deep learning and NLP. Its reliance on self-attention mechanisms allowed for more efficient and scalable processing of sequential data, paving the way for more powerful and sophisticated language models.

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"Attention Is All You Need" is a seminal paper in the field of natural language processing and machine learning, authored by Vaswani et al. in 2017. This paper introduced the Transformer model, which has since become a cornerstone in the development of advanced neural networks, particularly for tasks involving sequence data like language translation and text generation. Here's a brief overview of the key concepts: Key Concepts of "Attention Is All You Need" 1\. Transformer Architecture: \- The Transformer model replaces traditional sequence-based models like RNNs and LSTMs with a more efficient architecture based solely on self-attention mechanisms. \- It consists of an encoder and a decoder, each made up of multiple layers of self-attention and feedforward neural networks. 2\. Self-Attention Mechanism: \- Self-attention allows the model to weigh the importance of different words in a sequence relative to each other. \- It calculates three vectors for each word: Query (Q), Key (K), and Value (V). The attention score is computed by taking the dot product of Q and K, which is then used to weigh the V vectors. 3\. Multi-Head Attention: \- Instead of performing a single attention operation, the model uses multiple attention mechanisms (heads) in parallel. Each head focuses on different parts of the input sequence, capturing various aspects of the relationships between words. \- The outputs of these heads are concatenated and linearly transformed to produce the final attention output. 4\. Positional Encoding: \- Since Transformers do not have a built-in notion of the order of words, positional encodings are added to the input embeddings to provide information about the position of each word in the sequence. 5\. Feedforward Neural Networks: \- Each layer in the encoder and decoder has a feedforward neural network that processes the attention outputs. These networks are identical across layers but have different parameters. 6\. Residual Connections and Layer Normalization: \- Residual connections (additive connections) are used around each sub-layer (self-attention and feedforward networks) to help with the flow of gradients during training. \- Layer normalization is applied after each sub-layer to stabilize and accelerate the training process. Impact and Applications The Transformer model has revolutionized many NLP tasks, leading to the development of models like BERT, GPT, and T5. Its ability to handle long-range dependencies and parallelize training has made it a preferred choice for tasks such as: \- Machine Translation \- Text Summarization \- Question Answering \- Text Generation Conclusion The introduction of the Transformer model marked a significant advancement in the field of deep learning and NLP. Its reliance on self-attention mechanisms allowed for more efficient and scalable processing of sequential data, paving the way for more powerful and sophisticated language models.

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Day 74: Text Generation with RNNs and Transformers In the realm of Natural Language Processing (NLP), text generation stands as one of the most fascinating and challenging tasks. It involves creating coherent and contextually relevant text based on a given input. Recurrent Neural Networks (RNNs) RNNs are a type of neural network designed to recognize patterns in sequences of data, such as text, time series, or speech. Unlike traditional neural networks, RNNs have loops in them, allowing information to persist. This looping mechanism gives RNNs a kind of memory, making them suitable for tasks where the context of the previous input is crucial. Key Characteristics of RNNs: \- Sequential Processing: RNNs process inputs sequentially, making them ideal for tasks where order and sequence matter. \- Hidden States: Each step in the sequence updates a hidden state, which carries information from previous steps, allowing the network to maintain context. \- Backpropagation Through Time (BPTT): This is the training algorithm for RNNs, which involves unrolling the network through time and performing backpropagation on each time step. Despite their capabilities, RNNs suffer from limitations such as difficulty in capturing long-range dependencies due to vanishing and exploding gradient problems. This is where Long Short-Term Memory networks (LSTMs) and Gated Recurrent Units (GRUs) come into play, as they are designed to handle long-range dependencies more effectively. Transformers Transformers have transformed (pun intended) the landscape of NLP by addressing the limitations of RNNs. Introduced by Vaswani et al. in the paper “Attention is All You Need,” transformers leverage a mechanism called self-attention to process input data. Key Characteristics of Transformers: \- Self-Attention Mechanism: This allows the model to weigh the importance of different words in a sentence, regardless of their position. It enables the model to capture relationships between distant words more effectively. \- Parallel Processing: Unlike RNNs, transformers process all words in a sentence simultaneously, which significantly speeds up training and inference times. \- Encoder-Decoder Architecture: The standard transformer model consists of an encoder to process input sequences and a decoder to generate output sequences. Each layer in both the encoder and decoder has multiple self-attention heads that operate in parallel. Transformer Models in Practice: \- GPT (Generative Pre-trained Transformer): Developed by OpenAI, GPT models are designed for text generation tasks. GPT-3, the latest iteration, can generate human-like text and perform a wide array of language tasks with minimal prompt engineering. \- BERT (Bidirectional Encoder Representations from Transformers): BERT, developed by Google, is designed for understanding the context of words in a sentence. It’s pre-trained on a massive corpus and can be fine-tuned for various NLP tasks. 

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*What is RNN in Deep Learning? * RNN stands for Recurrent Neural Network. It's a type of neural network designed to handle sequential data, such as time series data, speech, text, and music. RNNs are particularly well-suited for modeling temporal dependencies between input sequences. The key characteristics of RNNs are: Recurrent connections: RNNs have feedback connections that allow the network to maintain a hidden state, which is an internal representation of the data. This hidden state is updated at each time step, allowing the network to capture temporal dependencies. Sequential processing: RNNs process input sequences one step at a time, updating the hidden state and output at each time step. Hidden state: The hidden state is an important component of RNNs, as it allows the network to remember information from previous time steps. Types of RNNs: Simple RNN: A basic RNN with a single layer of recurrent neurons. LSTM (Long Short-Term Memory): An RNN with a more complex architecture that includes memory cells, which help the network capture long-term dependencies. GRU (Gated Recurrent Unit): A simpler version of LSTM, with fewer parameters, but still effective for capturing temporal dependencies. RNNs have many applications, including: Language modeling: RNNs can be used to predict the next word in a sentence, given the context. Speech recognition: RNNs can be used to recognize spoken words and phrases. Time series forecasting: RNNs can be used to forecast future values in a time series. Natural language processing: RNNs can be used for a variety of NLP tasks, such as machine translation, sentiment analysis, and text classification. However, RNNs also have some limitations, including: Vanishing gradients: The gradients used to update the network during backpropagation can often become very small, making it difficult to train the network. Exploding gradients: In some cases, the gradients can become very large, causing the network to diverge. Difficulty in processing long-range dependencies: RNNs can struggle to capture long-range dependencies in sequential data. To address these limitations, researchers have developed new RNN architectures, such as Residual RNNs, Bidirectional RNNs, and Attention-based RNNs. Additionally, other types of neural networks, such as Convolutional Neural Networks (CNNs) and Transformers, have also been developed to handle sequential data.

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SWE: Generative AI | Empowering Businesses with Custom AI Agents to Unlock Innovation & Streamline Workflows | Transforming Automation & Efficiency at Zangoh 

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🔍 Exploring the Types of Recurrent Neural Networks (RNNs) 🔍 As we dive deeper into the world of deep learning, Recurrent Neural Networks (RNNs) stand out for their ability to handle sequential data and temporal dynamics. Whether it's language modeling, time series prediction, or sequence generation, RNNs have proven to be a powerful tool. Today, let's explore the different types of RNNs and their unique applications. 🌟 1\. Vanilla RNNs: \- The basic form of RNNs, Vanilla RNNs, maintain a memory of previous inputs through hidden states. Despite their simplicity, they struggle with long-term dependencies due to the vanishing gradient problem. 2\. Long Short-Term Memory Networks (LSTMs) \- LSTMs are designed to address the vanishing gradient problem, making them capable of learning long-term dependencies. They achieve this through a complex structure of gates (input, forget, and output gates) that regulate the flow of information. LSTMs are widely used in tasks like language translation, text generation, and speech recognition. 3\. Gated Recurrent Units (GRUs) \- GRUs are a simplified version of LSTMs, combining the input and forget gates into a single update gate. This streamlined architecture offers similar advantages to LSTMs but with fewer parameters, making them faster to train. GRUs are often preferred for real-time applications. 4\. Bidirectional RNNs \- Bidirectional RNNs process data in both forward and backward directions, capturing context from both past and future inputs. This dual perspective is particularly beneficial in tasks where context from both directions is crucial, such as named entity recognition and machine translation. 5\. Echo State Networks (ESNs) \- ESNs are a type of RNN with a sparsely connected hidden layer called the reservoir. They rely on a fixed, randomly initialized reservoir and only train the output weights. ESNs are efficient and excel in time series prediction. 6\. Attention Mechanisms \- Attention mechanisms enhance RNNs by allowing them to focus on specific parts of the input sequence when making predictions. This technique has revolutionized natural language processing tasks, leading to the development of models like the Transformer, which underpin many state-of-the-art NLP systems today. 💼 Applications of RNNs: \- Language Modeling: Predicting the next word in a sequence. \- Speech Recognition: Converting spoken language into text. \- Machine Translation: Translating text from one language to another. \- Time Series Prediction: Forecasting future values in a time series. \- Understanding the various types of RNNs and their strengths can help us choose the right model for our specific tasks, driving more accurate and efficient solutions. What are your experiences with RNNs? Share your thoughts and let's discuss! [Syed Yunus](https://in.linkedin.com/in/syedyunusds?trk=public_post-text) [Syed Nawaz](https://in.linkedin.com/in/syed-nawaz07?trk=public_post-text) [iNeuron.ai](https://in.linkedin.com/company/ineuron-a-i?trk=public_post-text) [PW (PhysicsWallah)](https://in.linkedin.com/company/physicswallah?trk=public_post-text) [Decoding Data Science](https://ae.linkedin.com/company/decodingdatascience?trk=public_post-text) [Towards AI](https://www.linkedin.com/company/towards-artificial-intelligence?trk=public_post-text) sudhanshu kumar [Youssef Hosni](https://fi.linkedin.com/in/youssef-hosni-b2960b135?trk=public_post-text) [#DeepLearning](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Fdeeplearning&trk=public_post-text) [#RNN](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Frnn&trk=public_post-text) [#MachineLearning](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Fmachinelearning&trk=public_post-text) [#DataScience](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Fdatascience&trk=public_post-text) [#AI](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Fai&trk=public_post-text)

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Understanding Recurrent Neural Networks (RNNs) for Named Entity Recognition (NER) In natural language processing, RNNs are widely used to solve sequence-based problems like Named Entity Recognition (NER). Here’s a breakdown of how RNNs work in this context: 1\. Input Representation: Sentences are tokenized, and each word is converted into vector representations using embeddings like Word2Vec, GloVe, or learnable embeddings. For example, the sentence “Harry Potter and Hermione Granger invented a new spell” would be transformed into a sequence of word vectors: x = [x^<1>, x^<2>, x^<3>, ... , x^<T>] 2\. Target Output: In NER, each word in a sentence is labeled as either an entity (like PERSON or ORGANIZATION) or O (for non-entity words). For example: “Harry Potter and Hermione Granger” y = [PERSON, PERSON, O, PERSON, PERSON] 3\. Prediction (ŷ): RNNs process each word in sequence, updating the hidden state at each time step, to predict whether the word is an entity or not. By learning from training data, the RNN aims to accurately match the target output. 4\. One-hot Encoding & Embeddings: For words in the vocabulary, one-hot encoding can be used before applying embeddings. Unknown words can be handled using a special token like UKN. 5\. Key RNN Functions: • Forward Propagation: Words are processed in sequence, updating the hidden state and generating predictions. • Back Propagation Through Time (BPTT): Errors in predictions are propagated back through the network to update weights, improving the model over time. What’s Next: Exploring Advanced RNN Models While standard RNNs are powerful, there are some limitations—such as their inability to fully capture context in long sentences or handle more complex patterns in data. This is where Bidirectional RNNs (BRNNs) and Deep RNNs come into play. In our next article, we’ll focus on: • BRNNs: These models process information in both directions (forward and backward), making them particularly useful when the context of both past and future words is critical for tasks like NER. • Deep RNNs: By stacking multiple RNN layers, Deep RNNs can capture more complex hierarchical patterns in data, leading to improved performance in various NLP tasks. Stay tuned for more insights into how these advanced models can help solve even more challenging problems in Natural Language Processing (NLP)! [#MachineLearning](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Fmachinelearning&trk=public_post-text) [#RNN](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Frnn&trk=public_post-text) [#DeepLearning](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Fdeeplearning&trk=public_post-text) [#BRNN](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Fbrnn&trk=public_post-text) [#NER](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Fner&trk=public_post-text) [#AI](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Fai&trk=public_post-text) [#DataScience](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Fdatascience&trk=public_post-text) [#NaturalLanguageProcessing](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Fnaturallanguageprocessing&trk=public_post-text)

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🧗♀️Tackling the Vanishing Gradient Problem in Neural Networks! 💥 🧠Series Day 1️⃣0️⃣/1️⃣0️⃣0️⃣ Deep learning 🤖 Ever wondered why your deep learning models sometimes struggle to learn? 🤔 It could be due to the vanishing gradient problem ! Here are some simple ways to mitigate it: 1️⃣Use ReLU Activation Functions 🚀 🧡ReLU (Rectified Linear Unit) helps keep the gradients from shrinking too much. Unlike sigmoid or tanh, ReLU doesn't squash the gradients, making it easier for the model to learn. 🟠Example: In image recognition tasks 🖼️, using ReLU allows models to better detect features like edges and textures. 2️⃣Batch Normalization 📊 💚This technique normalizes the input of each layer, stabilizing the learning process and reducing the problem of vanishing gradients. 🟢Example: In video analysis 🎥, batch normalization helps in processing sequences more effectively, enhancing action recognition. 3️⃣Weight Initialization 🏋️♀️ 💙Properly initializing weights can help. Techniques like Xavier or He initialization are designed to keep gradients in a healthy range. 🔵Example:In speech recognition 🎤, good weight initialization can lead to faster convergence and more accurate transcriptions. 4️⃣Gradient Clipping ✂️ 💛This involves setting a threshold to prevent gradients from becoming too large or too small, ensuring steady learning. 🟡Example:In financial modeling 💹, gradient clipping helps in predicting stock prices without the model diverging. 5️⃣Skip Connections 🔗 ❤️Used in architectures like ResNets, these connections allow gradients to bypass certain layers, helping them stay strong. 🔴Example In self-driving cars 🚗, skip connections improve the ability to recognize road signs and obstacles by maintaining strong gradient flows. 6️⃣Smarter Optimizers 🤖 💜 Optimizers like Adam or RMSprop adapt learning rates during training, preventing gradients from vanishing or exploding. 🟣Example: In natural language processing (NLP) 📚, these optimizers enhance the model's ability to understand and generate human language. 📝Summary Mitigating the vanishing gradient problem is crucial for the success of deep learning models. By using techniques like ReLU, batch normalization, weight initialization, gradient clipping, skip connections, and smarter optimizers, u can ensure your models learn more effectively and efficiently. If u found this post helpful, don't forget to like👍, and repost.♻️ [#DeepLearning](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Fdeeplearning&trk=public_post-text) [#MachineLearning](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Fmachinelearning&trk=public_post-text) [#DataScience](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Fdatascience&trk=public_post-text) [#Techmemes](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Ftechmemes&trk=public_post-text) [#NeuralNetworks](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Fneuralnetworks&trk=public_post-text) [#GradientDescent](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Fgradientdescent&trk=public_post-text) [#ArtificialIntelligence](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Fartificialintelligence&trk=public_post-text)

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Software Engineer @Google | ex-Associate data engineer @CVSHealth | Data Scientist, Machine Learning Engineer, LLM enthusiast | 

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Recurrent Neural Networks (RNNs): A Dive into Sequential Data Processing Before Transformers revolutionized AI, Recurrent Neural Networks (RNNs) were the go-to models for handling sequential data like text, speech, and time-series tasks. RNNs excel when the order of inputs matters, making them especially useful for Natural Language Processing (NLP) 🗣️. But training RNNs isn’t without its challenges. Let’s break it down: ⚙️ RNN Components: 1\. 📥 Input Layer: For NLP tasks, the input is a sequence of words from a sentence or review. Example: “The movie was fantastic! The acting was top-notch.” 2\. 🔠 Embedding Layer: Words are converted into dense vectors that capture semantic meaning. Example: \- "The" → [0.2, 0.1, 0.5,...] \- "movie" → [0.3, 0.4, 0.2,...] 3\. 🧠 Hidden State: RNNs process each word, generating a hidden state that reflects the context of the current and previous words. Example: Processing "fantastic" builds on the context of earlier words in the sentence. 4\. 🔄 Recurrent Connection: Hidden states are updated step-by-step, helping the RNN "remember" prior context as it moves through the sequence. 5\. 🔗 Output Layer: The final hidden state is used to make predictions, such as classifying IMDb reviews as positive or negative. 🛠️ Training the RNN: 1\. ➡️ Forward Pass: Feed the sequence into the RNN, update hidden states, and generate predictions. Example: Input: "The movie was fantastic!" → Output: Positive Sentiment. 2\. 🔙 Backpropagation Through Time (BPTT): RNN weights are updated by unfolding the network over time. Gradients are computed by propagating the error backward through each timestep. However, BPTT can suffer from vanishing gradients, making it tough for the model to learn from long sequences. ⚠️ Key Challenges of RNNs: \- ⏳ Sequential Processing: RNNs process data one step at a time, making them slower than modern models that leverage parallel processing. \- 📉 Vanishing Gradients: When dealing with long sequences, RNNs struggle to capture long-term dependencies, as gradients diminish over time. Despite these limitations, RNNs have played a significant role in advancing AI's ability to process sequential data. In upcoming posts, we’ll explore how LSTMs and GRUs address the challenges of RNNs, and we'll begin our understanding of Gen-AI, LLMs, and their applications. Check out this link: [https://lnkd.in/gDCU22G7](https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Flnkd%2Ein%2FgDCU22G7&urlhash=JN4I&trk=public_post-text) for the code to build an RNN from scratch, specifically designed to work with the IMDb dataset. 💬 What are your thoughts on RNNs versus modern architectures like Transformers? Let’s discuss in the comments! Image source: [https://lnkd.in/gMHgpz9w](https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Flnkd%2Ein%2FgMHgpz9w&urlhash=Necb&trk=public_post-text) [#AI](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Fai&trk=public_post-text) [#MachineLearning](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Fmachinelearning&trk=public_post-text) [#NLP](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Fnlp&trk=public_post-text) [#RNN](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Frnn&trk=public_post-text) [#BPTT](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Fbptt&trk=public_post-text) [#TechInnovation](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Ftechinnovation&trk=public_post-text) [#DeepLearning](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Fdeeplearning&trk=public_post-text) [#LLMOps](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Fllmops&trk=public_post-text) [#LLMSeries](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2Fllmseries&trk=public_post-text)

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Gen AI Learning - Day 20 of 30 day challenge : 🤖 What is an Artificial Neural Network? 🌐 An Artificial Neural Network (ANN) is a computational model that mimics the way biological neural networks in the human brain operate. ANNs consist of layers of interconnected nodes (or neurons), which process and transmit information. These layers include an input layer, one or more hidden layers, and an output layer. 🔑 Key Components of ANNs: 🧠 Neurons: The basic units of an ANN, analogous to neurons in the brain. Each neuron receives inputs, processes them, and produces an output. 📚 Layers: ANNs are organized into layers. The input layer receives the initial data, hidden layers process the data through complex transformations, and the output layer generates the final prediction or decision. 🥌 Weights and Biases: Connections between neurons have weights, which are adjusted during training to minimize errors. Biases are added to the weighted sum of inputs to help the model learn complex patterns. 💎 Activation Functions: These functions determine whether a neuron should be activated or not, introducing non-linearity into the model and allowing it to learn complex patterns. Common activation functions include sigmoid, tanh, and ReLU (Rectified Linear Unit). 🛠️ How Do ANNs Work? 📅 Data Input: Data is fed into the input layer of the network. ⏩ Forward Propagation: Data passes through the network layers, with each neuron applying weights and activation functions to generate outputs. 📲 Loss Calculation: The output is compared to the actual result to calculate the loss (error). ⬅ Backpropagation: The network adjusts its weights using backpropagation, a process that involves calculating the gradient of the loss function and updating the weights to minimize the error. 🔃Iteration: This process is repeated over many iterations (epochs) until the network's performance is optimized. 🚀 Applications of ANNs ANNs have a wide range of applications across various industries due to their ability to learn and generalize from data: Image and Speech Recognition: Used in facial recognition systems, speech-to-text applications, and handwriting recognition. 👨✈️ Natural Language Processing (NLP): Powering chatbots, language translation services, and sentiment analysis tools. 🏥 Healthcare: Assisting in disease diagnosis, personalized treatment plans, and medical image analysis. 💰 Finance: Used for credit scoring, fraud detection, and algorithmic trading. Autonomous Vehicles: Enabling self-driving cars to recognize objects and make driving decisions. 🌟 Advantages of ANNs 🙇♂️ Adaptability: ANNs can learn and adapt to new data without requiring explicit programming. 😯 Handling Complexity: Capable of modeling complex, non-linear relationships in data. ✍ Generalization: Can generalize from training data to make accurate predictions on new, unseen data. Keep Learning 👨🏫 Follow [Santhosh Tirunahari](https://in.linkedin.com/in/santhoshtirunahari?trk=public_post-text) for more such knowledge bites.

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Deep learning procedures typically involve several key steps, each essential for building and training models effectively. Here's a general overview: 1\. Data Collection and Preparation Gather Data: Collect relevant data, which could include images, text, audio, or other types of data. Data Augmentation: For tasks like image recognition, data augmentation can help by creating variations of the data (e.g., rotating images, flipping, etc.). 2\. Model Selection Choose Model Architecture: Select a deep learning model suitable for the problem. Common types include: Convolutional Neural Networks (CNNs) for image-based tasks. Recurrent Neural Networks (RNNs), LSTMs, or GRUs for sequential data (e.g., time-series or natural language). Transformers for natural language processing tasks. Pre-trained Models: You might also choose to use pre-trained models (like ResNet, GPT, etc.) and fine-tune them on your task. 3\. Model Design Layer Architecture: Design the network’s layers (e.g., number of layers, types of layers like convolutional, pooling, fully connected, etc.). Activation Functions: Choose activation functions like ReLU, Sigmoid, or Tanh to introduce non-linearities into the model. Loss Function: Depending on the task, you might choose a loss function (e.g., cross-entropy for classification or mean squared error for regression). Optimizer: Choose an optimizer (e.g., SGD, Adam) to adjust weights during training. 4\. Training the Model Forward Propagation: Pass the input data through the model to get predictions. Backpropagation: Compute the gradient of the loss function with respect to each weight, then update the weights using the chosen optimizer. Batch Processing: Often, data is divided into mini-batches to speed up training and avoid memory issues. Epochs: Train the model over multiple epochs (iterations over the full dataset). 5\. Hyperparameter Tuning Learning Rate: Tune the learning rate, which controls how much to adjust the weights at each step. Batch Size: Adjust the batch size (number of training samples per update). Number of Layers, Neurons: Experiment with model depth (more layers) and width (more neurons per layer) to improve performance. 6\. Model Evaluation Validation: Use a validation set to monitor the model's performance during training and prevent overfitting. Metrics: Evaluate model performance using appropriate metrics (e.g., accuracy, precision, recall, F1-score for classification, mean absolute error for regression). Cross-Validation: Use techniques like k-fold cross-validation to assess how the model generalizes to unseen data. 7\. Model Optimization Regularization: Techniques like dropout or L2 regularization help prevent overfitting by reducing the complexity of the model. Early Stopping: Stop training if the model’s performance on the validation set stops improving. Fine-Tuning: Adjust hyperparameters or tweak the model architecture based on evaluation metrics to improve performance.

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What are some examples of advanced artificial intelligence algorithms currently being utilized? Advanced artificial intelligence (AI) algorithms are revolutionizing various fields with their ability to handle complex data, make predictions, and automate processes. Here are some examples of sophisticated AI algorithms currently in use: 1\. Deep Learning Algorithms Examples: Convolutional Neural Networks (CNNs): Used for image recognition and computer vision tasks, CNNs excel at identifying patterns and features in visual data. They are widely used in applications such as facial recognition, autonomous vehicles, and medical image analysis. Recurrent Neural Networks (RNNs): RNNs are effective for sequential data and time-series analysis, such as natural language processing (NLP) and speech recognition. Variants like Long Short-Term Memory (LSTM) networks address issues related to learning long-term dependencies. 2\. Generative Adversarial Networks (GANs) Examples: Image Synthesis: GANs are used to create realistic images from scratch or modify existing images, such as in the creation of deepfakes or synthetic media. For example, StyleGAN generates high-quality images of faces that do not belong to real individuals. Data Augmentation: GANs can generate additional data to enhance training datasets, which is useful in fields with limited data availability. 3\. Reinforcement Learning (RL) Algorithms Examples: Deep Q-Networks (DQNs): Used for decision-making in complex environments, such as game playing and robotics. DQNs combine Q-learning with deep learning to approximate the value function in reinforcement learning. Proximal Policy Optimization (PPO): PPO is an advanced RL algorithm used for training policies in environments where stable and efficient learning is crucial. It is commonly used in robotics and game environments. 4\. Evolutionary Algorithms Examples: Genetic Algorithms (GAs): Inspired by natural evolution, GAs are used for optimization problems and feature selection. They simulate the process of natural selection to find optimal or near-optimal solutions. 5\. Bayesian Methods Examples: Bayesian Networks: These probabilistic graphical models represent variables and their conditional dependencies using a directed acyclic graph. They are used in medical diagnosis, risk assessment, and decision-making processes. Gaussian Processes: Used for regression and classification tasks, Gaussian Processes provide probabilistic predictions and are effective in scenarios where uncertainty quantification is important. 6\. Graph Neural Networks (GNNs) Examples: Node Classification: GNNs are used to classify nodes in a graph, which is useful in social network analysis and fraud detection. Link Prediction: GNNs predict missing links or connections in graphs, applied in recommendation systems and knowledge graph completion.

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url: http://arxiv.org/abs/2306.07303v1
title: [2306.07303v1] A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks
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# Computer Science > Machine Learning

**arXiv:2306.07303v1** (cs) 

[Submitted on 11 Jun 2023]

# Title:A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks

Authors:[Saidul Islam](https://arxiv.org/search/cs?searchtype=author&query=Islam,+S), [Hanae Elmekki](https://arxiv.org/search/cs?searchtype=author&query=Elmekki,+H), [Ahmed Elsebai](https://arxiv.org/search/cs?searchtype=author&query=Elsebai,+A), [Jamal Bentahar](https://arxiv.org/search/cs?searchtype=author&query=Bentahar,+J), [Najat Drawel](https://arxiv.org/search/cs?searchtype=author&query=Drawel,+N), [Gaith Rjoub](https://arxiv.org/search/cs?searchtype=author&query=Rjoub,+G), [Witold Pedrycz](https://arxiv.org/search/cs?searchtype=author&query=Pedrycz,+W)

View a PDF of the paper titled A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks, by Saidul Islam and 6 other authors

[View PDF](/pdf/2306.07303v1)

> Abstract:Transformer is a deep neural network that employs a self-attention mechanism to comprehend the contextual relationships within sequential data. Unlike conventional neural networks or updated versions of Recurrent Neural Networks (RNNs) such as Long Short-Term Memory (LSTM), transformer models excel in handling long dependencies between input sequence elements and enable parallel processing. As a result, transformer-based models have attracted substantial interest among researchers in the field of artificial intelligence. This can be attributed to their immense potential and remarkable achievements, not only in Natural Language Processing (NLP) tasks but also in a wide range of domains, including computer vision, audio and speech processing, healthcare, and the Internet of Things (IoT). Although several survey papers have been published highlighting the transformer's contributions in specific fields, architectural differences, or performance evaluations, there is still a significant absence of a comprehensive survey paper encompassing its major applications across various domains. Therefore, we undertook the task of filling this gap by conducting an extensive survey of proposed transformer models from 2017 to 2022. Our survey encompasses the identification of the top five application domains for transformer-based models, namely: NLP, Computer Vision, Multi-Modality, Audio and Speech Processing, and Signal Processing. We analyze the impact of highly influential transformer-based models in these domains and subsequently classify them based on their respective tasks using a proposed taxonomy. Our aim is to shed light on the existing potential and future possibilities of transformers for enthusiastic researchers, thus contributing to the broader understanding of this groundbreaking technology. 

Subjects: |  Machine Learning (cs.LG); Computation and Language (cs.CL)  
---|---  
Cite as: | [arXiv:2306.07303](https://arxiv.org/abs/2306.07303) [cs.LG]  
  | (or  [arXiv:2306.07303v1](https://arxiv.org/abs/2306.07303v1) [cs.LG] for this version)   
  |  <https://doi.org/10.48550/arXiv.2306.07303> Focus to learn more arXiv-issued DOI via DataCite  
  
## Submission history

From: Gaith Rjoub [[view email](/show-email/6bb66a71/2306.07303)]   
**[v1]** Sun, 11 Jun 2023 23:13:51 UTC (3,462 KB)  


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url: http://arxiv.org/abs/2008.05282v1
title: [2008.05282v1] Text Classification based on Multi-granularity Attention Hybrid Neural Network
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# Computer Science > Computation and Language

**arXiv:2008.05282v1** (cs) 

[Submitted on 12 Aug 2020]

# Title:Text Classification based on Multi-granularity Attention Hybrid Neural Network

Authors:[Zhenyu Liu](https://arxiv.org/search/cs?searchtype=author&query=Liu,+Z), [Chaohong Lu](https://arxiv.org/search/cs?searchtype=author&query=Lu,+C), [Haiwei Huang](https://arxiv.org/search/cs?searchtype=author&query=Huang,+H), [Shengfei Lyu](https://arxiv.org/search/cs?searchtype=author&query=Lyu,+S), [Zhenchao Tao](https://arxiv.org/search/cs?searchtype=author&query=Tao,+Z)

View a PDF of the paper titled Text Classification based on Multi-granularity Attention Hybrid Neural Network, by Zhenyu Liu and 4 other authors

[View PDF](/pdf/2008.05282v1)

> Abstract:Neural network-based approaches have become the driven forces for Natural Language Processing (NLP) tasks. Conventionally, there are two mainstream neural architectures for NLP tasks: the recurrent neural network (RNN) and the convolution neural network (ConvNet). RNNs are good at modeling long-term dependencies over input texts, but preclude parallel computation. ConvNets do not have memory capability and it has to model sequential data as un-ordered features. Therefore, ConvNets fail to learn sequential dependencies over the input texts, but it is able to carry out high-efficient parallel computation. As each neural architecture, such as RNN and ConvNets, has its own pro and con, integration of different architectures is assumed to be able to enrich the semantic representation of texts, thus enhance the performance of NLP tasks. However, few investigation explores the reconciliation of these seemingly incompatible architectures. To address this issue, we propose a hybrid architecture based on a novel hierarchical multi-granularity attention mechanism, named Multi-granularity Attention-based Hybrid Neural Network (MahNN). The attention mechanism is to assign different weights to different parts of the input sequence to increase the computation efficiency and performance of neural models. In MahNN, two types of attentions are introduced: the syntactical attention and the semantical attention. The syntactical attention computes the importance of the syntactic elements (such as words or sentence) at the lower symbolic level and the semantical attention is used to compute the importance of the embedded space dimension corresponding to the upper latent semantics. We adopt the text classification as an exemplifying way to illustrate the ability of MahNN to understand texts. 

Subjects: |  Computation and Language (cs.CL); Artificial Intelligence (cs.AI)  
---|---  
Cite as: | [arXiv:2008.05282](https://arxiv.org/abs/2008.05282) [cs.CL]  
  | (or  [arXiv:2008.05282v1](https://arxiv.org/abs/2008.05282v1) [cs.CL] for this version)   
  |  <https://doi.org/10.48550/arXiv.2008.05282> Focus to learn more arXiv-issued DOI via DataCite  
  
## Submission history

From: Zhenyu Liu [[view email](/show-email/dd7eba43/2008.05282)]   
**[v1]** Wed, 12 Aug 2020 13:02:48 UTC (757 KB)  


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url: http://arxiv.org/abs/1905.11558v1
title: [1905.11558v1] Leap-LSTM: Enhancing Long Short-Term Memory for Text Categorization
scrape_date: 2025-03-12T12:20:20.748769
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# Computer Science > Computation and Language

**arXiv:1905.11558v1** (cs) 

[Submitted on 28 May 2019]

# Title:Leap-LSTM: Enhancing Long Short-Term Memory for Text Categorization

Authors:[Ting Huang](https://arxiv.org/search/cs?searchtype=author&query=Huang,+T), [Gehui Shen](https://arxiv.org/search/cs?searchtype=author&query=Shen,+G), [Zhi-Hong Deng](https://arxiv.org/search/cs?searchtype=author&query=Deng,+Z)

View a PDF of the paper titled Leap-LSTM: Enhancing Long Short-Term Memory for Text Categorization, by Ting Huang and 1 other authors

[View PDF](/pdf/1905.11558v1)

> Abstract:Recurrent Neural Networks (RNNs) are widely used in the field of natural language processing (NLP), ranging from text categorization to question answering and machine translation. However, RNNs generally read the whole text from beginning to end or vice versa sometimes, which makes it inefficient to process long texts. When reading a long document for a categorization task, such as topic categorization, large quantities of words are irrelevant and can be skipped. To this end, we propose Leap-LSTM, an LSTM-enhanced model which dynamically leaps between words while reading texts. At each step, we utilize several feature encoders to extract messages from preceding texts, following texts and the current word, and then determine whether to skip the current word. We evaluate Leap-LSTM on several text categorization tasks: sentiment analysis, news categorization, ontology classification and topic classification, with five benchmark data sets. The experimental results show that our model reads faster and predicts better than standard LSTM. Compared to previous models which can also skip words, our model achieves better trade-offs between performance and efficiency. 

Comments: | Accepted by IJCAI 2019, 7 pages, 3 figures  
---|---  
Subjects: |  Computation and Language (cs.CL)  
Cite as: | [arXiv:1905.11558](https://arxiv.org/abs/1905.11558) [cs.CL]  
  | (or  [arXiv:1905.11558v1](https://arxiv.org/abs/1905.11558v1) [cs.CL] for this version)   
  |  <https://doi.org/10.48550/arXiv.1905.11558> Focus to learn more arXiv-issued DOI via DataCite  
  
## Submission history

From: Ting Huang [[view email](/show-email/72f79eeb/1905.11558)]   
**[v1]** Tue, 28 May 2019 01:15:11 UTC (357 KB)  


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url: http://arxiv.org/abs/1906.10720v2
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[1906.10720v2] Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics

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# Computer Science > Machine Learning

**arXiv:1906.10720v2** (cs) 

[Submitted on 25 Jun 2019 ([v1](https://arxiv.org/abs/1906.10720v1)), last revised 4 Dec 2019 (this version, v2)]

# Title:Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics

Authors:[Niru Maheswaranathan](https://arxiv.org/search/cs?searchtype=author&query=Maheswaranathan,+N), [Alex Williams](https://arxiv.org/search/cs?searchtype=author&query=Williams,+A), [Matthew D. Golub](https://arxiv.org/search/cs?searchtype=author&query=Golub,+M+D), [Surya Ganguli](https://arxiv.org/search/cs?searchtype=author&query=Ganguli,+S), [David Sussillo](https://arxiv.org/search/cs?searchtype=author&query=Sussillo,+D)

View a PDF of the paper titled Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics, by Niru Maheswaranathan and 4 other authors

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> Abstract:Recurrent neural networks (RNNs) are a widely used tool for modeling sequential data, yet they are often treated as inscrutable black boxes. Given a trained recurrent network, we would like to reverse engineer it--to obtain a quantitative, interpretable description of how it solves a particular task. Even for simple tasks, a detailed understanding of how recurrent networks work, or a prescription for how to develop such an understanding, remains elusive. In this work, we use tools from dynamical systems analysis to reverse engineer recurrent networks trained to perform sentiment classification, a foundational natural language processing task. Given a trained network, we find fixed points of the recurrent dynamics and linearize the nonlinear system around these fixed points. Despite their theoretical capacity to implement complex, high-dimensional computations, we find that trained networks converge to highly interpretable, low-dimensional representations. In particular, the topological structure of the fixed points and corresponding linearized dynamics reveal an approximate line attractor within the RNN, which we can use to quantitatively understand how the RNN solves the sentiment analysis task. Finally, we find this mechanism present across RNN architectures (including LSTMs, GRUs, and vanilla RNNs) trained on multiple datasets, suggesting that our findings are not unique to a particular architecture or dataset. Overall, these results demonstrate that surprisingly universal and human interpretable computations can arise across a range of recurrent networks. 

Comments: | Presented at NeurIPS 2019  
---|---  
Subjects: |  Machine Learning (cs.LG); Machine Learning (stat.ML)  
Cite as: | [arXiv:1906.10720](https://arxiv.org/abs/1906.10720) [cs.LG]  
  | (or  [arXiv:1906.10720v2](https://arxiv.org/abs/1906.10720v2) [cs.LG] for this version)   
  |  <https://doi.org/10.48550/arXiv.1906.10720> Focus to learn more arXiv-issued DOI via DataCite  
  
## Submission history

From: Niru Maheswaranathan [[view email](/show-email/e94dca5d/1906.10720)]   
**[[v1]](/abs/1906.10720v1)** Tue, 25 Jun 2019 18:42:25 UTC (5,870 KB)  
**[v2]** Wed, 4 Dec 2019 20:51:14 UTC (5,871 KB)  


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Table of Contents

# Text Analysis Turbocharge: LSTMs, RNNs, and Transformers!

Machine Learning

December 20, 20235 min

In the e­volving landscape of evidence­-based decision-making, text analysis has emerged as a conside­rable part, changing the way we derive understanding from huge amounts of written information. As we explore the information age, wherever textual data multiplied across interpersonal communication, business interaction, and more, the skill to interpret and deduce useful information from text has turned out to be crucial.

## **Significance of Text Analysis**

Unstructured text can contain valuable information, which can be extracted through text analysis, also known as[ natural language processing](https://www.codiste.com/natural-language-processing-development) (NLP). With diverse applications such as sentiment analysis for public opinion, summarization for quick understanding, and entity recognition for key element identification, text analysis is essential for informed decisions. It enables individuals, businesses, and researchers to derive significant insights.  
  
The latent for unstructured data is immense: news articles, social media posts, academic papers, customer feedback - the opportunities are limitless. Text analysis transforms this treasure trove of information into practical knowledge. Businesses use it for studying market trends, competitor awareness, and customer reviews. Meanwhile, in academic circles, it expedites literature reviews and furthers understanding. Such applications are as varied as the data they operate on.

## **Key Technologies: LSTMs, RNNs, and Transformers**

Three technologies that offer distinct linguistic processing solutions are Long Short-Term Memory networks, Recurrent Neural Networks, and Transformers. These three key advancements in text analytics have one common goal: progressing text analysis capabilities.

  * LSTMs (Long Short-Term Memory Networks)  
Recurrent neural networks known as LSTMs were designed to solve the vanishing gradient problem, an issue that hindered the capture of sequential data's long-range dependencies. LSTMs excel at context preservation, resulting in their widespread use in sentiment analysis, machine translation, and language modelling.
  * RNNs (Recurrent Neural Networks)  
RNNs boast excellence in sequential data processing by retaining a concealed state that memorises previous steps. Despite being currently overlooked, RNNs are valuable, particularly in problems necessitating consecutive analysis, like forecasting periodic data and detecting handwriting.
  * Transformers  
Introduced in the paper "Attention is All You Need" by Vaswani et al., transformers have completely transformed [NLP](https://www.codiste.com/deep-learning-exploring-neural-networks-deep-neural-networks). Exploiting advanced mechanisms to process input data in parallel, transformers excel at tasks involving long-range dependencies, making them extremely efficient. The impact of transformers has been a real game-changer, marking a paradigm shift that has resulted in the creation of revolutionary [ML models](https://www.codiste.com/machine-learning-model-development) like BERT, and [GPT](https://www.codiste.com/what-difference-between-gpt-gpt3-gpt-3-5-gptturbo-gpt-4). These models have raised the bar for all kinds of NLP tasks, setting new benchmarks that have never been achieved before.



## Understanding of Text Analysis

Text analysis, a mighty tool utilised across various global industries, plays a vital role in unveiling the secrets concealed within words. Stories derive from individual words, and essential meanings stem from phrases. Therefore, to fully comprehend text analysis, we must first understand its significance.

#### What is Text Analysis?

Meaningful information is extracted from human language using computational techniques in text analysis or [natural language processing ](https://www.codiste.com/neural-network-optimization-techniques-faster-better-training)(NLP). When it comes to tasks of content creation, options are many. From straightforward text classification to the de­licate art of sentiment analysis, the opportunities seem countless. But hold on tight, because the real adventure begins with the more intricate pursuits: e­ntity recognition, summarization, and language translation.  
  
In this fascinating world where artificial intelligence and text intertwine, text analysis e­merges as the ultimate­ bridge connecting machines and human communication. The convergence­ of AI and language exposes potentials beyond our conceptions and imagination.

#### **Importance of Text Analysis**

In today's fast-paced world, where information overload is the name­ of the game, businesse­s, decision-makers, and researchers find solace in the proficiency of text analysis. This formidable tool enables them to get the best out of the heaps of unstructure­d textual data. By carefully dissecting and organising this tangle­d web of information, valuable insights eme­rge, ready to shape the path forward.  
When it comes to data analysis, professionals have used the capability of text analysis—a noteworthy tool that swiftly and precisely e­xtracts vital information. 

#### **Real-world Applications in Various Industries**

Across various industries and sectors, the adaptability and usefulness of text analysis can be felt.

  1. Healthcare  
Text analysis plays a vital role in healthcare by extracting crucial information from patient records to improve clinical documentation, diagnosis, and treatment planning. It also serves as an essential public health monitoring tool by analysing disease trends and outbreaks through social media analysis.
  2. Financial Institutions  
Financial institutions use text analysis as a powerful tool to evaluate market sentiment across financial reports, news articles, and social media. The gathered data provides actionable insights to make informed investment decisions and manage risks more effectively.
  3. Legal Services  
In recent years, legal and compliance functions have grown more vital for law firms. Advanced text analysis now assists firms in accurately and efficiently navigating intricate legal settings. Monitoring further aids due diligence for clients and contract review, which are essential for staying compliant amid multiplying regulations.



## **Sequential Understanding of Long Short-Term Memory (LSTMs)**

In the world of neural networks, Long Short-Term Me­mory networks, or LSTMs, stood as a cornerstone for proce­ssing sequential data, clarifying the intricate depende­ncies embedde­d in linguistic structures.

#### What are LSTMs?

A long short-term memory (LSTM) network is a type of artificial neural network designed to understand and process sequences of data. Imagine teaching a computer to predict the next word in a sentence or understand the emotion in a paragraph. LSTMs are particularly useful in these scenarios because of their exceptional ability to remember and use information through a series of steps, overcoming the limitations of traditional neural networks.

#### **Examples of How LSTMs Are Used in Text Analysis**

  1. LSTM in Sentiment Analysis  
Capturing contextual nuances and dependencies in language, LSTMs excel in discerning sentiments, making them a notable application in sentiment analysis. Depending on the situation, this enables companies to get feedback from customers about many things relying on sentiment analysis done by the network through processing textual information.
  2. LSTM in Language Translation  
In language translation formulas, LSTMs are key figures due to their capacity to comprehend and retain sentence context. This leads to more consistent and accurate translations with consideration of the larger meaning, such as within Google Translate.
  3. LSTM in Named Entity Recognition (NER)  
Providing sequential comprehension to named entity recognition, LSTM is the desirable method to distinguish and group entities like people's names, organisations, or places in unstructured text. This technique facilitates the efficient extraction and classification of these entities.



#### **Advantages of LSTMs**

LSTMs have a knack for comprehending and memorising sequences of information, which makes them great for text analysis in several fields. Therefore, they are useful in important dependency tasks such as sentiment analysis as well as language translation since LSTMs have good context preservation across long sequences. Handling different aspects of language, LSTMs exhibit their versatility in adapting to a wide range of NLP tasks.

#### **Limitations of LSTMs**

LSTMs can be challenging to train due to their computational complexity, particularly for larger datasets and limited resources. This can result in significant time constraints, making it difficult to complete training in a timely manner.  
  
The training times for LSTMs are long due to their deep architecture, especially when compared to more straightforward models. During training, the scalability of LSTMs may be hindered due to their limited parallelization capabilities caused by their sequential nature.

## **Recurrent Neural Networks (RNNs)**

RNNs play a key role­ in understanding sequential data like text. Their architecture allows them to remember previous information as they read through se­quences. This helps RNNs identify patterns over time better than other neural ne­tworks. RNNs look at pieces of a seque­nce one at a time. Each time­ they see a new piece, they update­ their internal repre­sentation of what they've se­en so far. They use this to pre­dict what comes next.  
  
RNNs represent a variety of neural ne­tworks created for tasks involving data with order and conte­xt significance. What distinguishes RNNs is their skill to maintain an unse­en state, serving as re­collection carrying details betwe­en periods. This intrinsic me­mory process empowers RNNs to notice temporal connections, making them apt for tasks such as language modelling, spee­ch interpretation, and certainly, te­xt evaluation.

#### **Examples of How RNNs Are Used in Text Analysis**

One important use of RNNs is in text generation. The context provided by previous words is used by the network to forecast the following word in a sequence.  
  
Language modelling is an area where recurrent neural networks shine as they can predict the probability of occurrence of a word given its previous context. The use of RNN-based language models is crucial in machine translation and speech recognition.  
  
RNNs have predictive applications beyond text analysis such as predicting weather patterns or stock prices. The network comprehends the series of events in time and its relationship, which boosts forecasting accuracy.

#### **Comparative Analysis with LSTMs**

Introducing additional architectural elements that tackle the drawbacks of traditional RNNs, LSTMs, and RNNs have a shared foundation of handling sequential data. However, the dissimilarity resides in the technique used to manage prior temporal information.  
  
Incorporating memory cells and gating mechanisms, LSTMs can selectively remember or discard information over extended sequences, thereby solving the vanishing gradient problem and improving their ability to capture long-range dependencies.  
  
The hidden state of RNNs is what maintains short-term memory, as opposed to other networks. However, this can lead to difficulties with longer sequences, as relevant information may need to be preserved.

#### **Use Cases in Text Analysis**

Sentiment analysis tasks employ RNNs to decipher the sentiment expressed in textual data, similar to LSTMs. The nuanced sentiments are better discerned due to the sequential understanding of RNNs.  
  
In text analysis, RNN can be used to enhance NER, a process that aims to recognize and classify entities. Utilising its sequential processing, the network can accurately extract and categorise the entities.  
  
Generating clear and compact summaries of longer texts is a crucial task in abstractive text summarization, for which RNNs are used. They enable the network to grasp complex information and make it easier to comprehend quickly.

## **The Rise of Transformers in Natural Language Processing**

Transformers have emerged in the ever-changing field of natural language processing (NLP), redefining the understanding and processing of languages by machines. This segment starts with an exploration of transformers, which bring transformation to NLP.  
  
Introduced by Vaswani et al. in their influential paper, “Attention Is All You Need,” Transformers differ from typical sequence-to-sequence models such as RNNs and LSTMs. The main difference is in the attention mechanism that allows the model to consider all the words of a sequence simultaneously through self-attention. The ability to process multiple sentences at once significantly reduces training and inference times, thus making Transformers good for tasks involving long-term dependencies.  
  
Transformers can relate words irrespective of the circumstances they hold within a sequence, thus performing much better than RNNs or LSTMs. This alteration in design not only helps improve performance but also has broader implications for applications related to NLP.

#### **Impact on Natural Language Processing (NLP)**

Transformers have been the most influential product in NLP that has revolutionised it and has given new standards of performance in various language-based tasks. The models have shown unprecedented abilities to understand context, generate coherent text, and beat their predecessors in machine translation, sentiment analysis, question-answering, and other such tasks.

  * Efficient Parallelisation  
Transformers can be highly parallelized, thus allowing for efficient training on powerful hardware like GPUs and TPUs. This makes it feasible for a large number of modules to be run significantly reducing the time taken to train large-scale language models.
  * Long-Range Dependencies  
The self-attention mechanism helps transformers capture long-range dependencies in a sequence thereby overcoming problems faced by traditional sequential models. They are particularly useful for complex language-related tasks where context understanding is crucial.
  * Transfer Learning  
Transformers are great for transfer learning in NLP. Pretrained models can be fine-tuned into specific tasks capitalising on well-known information from massive amounts of unlabeled data. Such a transfer learning approach has given rise to pioneering models such as BERT and GPT.



#### **Explanation of Self-Attention Mechanisms**

In the Transformer architecture, the self-attention mechanism is vital as it allows the model to dynamically assign weight to different words in a sequence. This differs from traditional models that process each word one at a time because self-attention enables each word to focus on all the others concurrently.  
  
This way, the model can concentrate on the important words for every word in a series and capture intricate links between them.

#### **Highlighting BERT and GPT-3**

  * BERT (Bidirectional Encoder Representations from Transformers)  
Google introduced BERT to revolutionise NLP. One unique aspect of this is that it is bidirectional, meaning words’ meanings depend on both contexts to their left and right. This has extremely supported question-answering as well as named entity recognition tasks. Many NLP applications rely heavily on BERT’s pre-trained embeddings which are contextualised.
  * GPT-3 (Generative Pre-trained Transformer 3)  
OpenAI developed GPT-3 as one of the largest and most powerful language models in history so far. Made up of an incredible number of 175 billion parameters, GPT-3 has indeed proved to be a great language generator where it comfortably writes sentences, paragraphs, and even full articles with a fluency that can be easily mistaken for human language. It offers a wide range of use from translation and summarization to creative writing and code generation.



## **LSTMs, RNNs, and Transformers as Real-world Impact**

Let us now explore the practical applications of LSTMs, RNNs, and Transformers in real-world scenarios. Each technology comes with its strengths and weaknesses that make them dominant in different industries or fields and as such their contributions towards progression in text analysis can be well understood.

## **Real Case Study of Long Short-Term Memory (LSTM)**

#### **Enhancing Customer Experience in E-commerce**

#### Advantages

  * Retention of Context  
LSTMs are suitable for sentiment analysis tasks that require taking context into account due to their ability to capture long-range dependencies. For example, an e-commerce giant implemented LSTMs in analyzing product reviews holistically taking cognizance of the sequential nature of customer feedback.
  * Sophisticated Sentiment Understanding  
Instead of merely classifying a review as positive or negative, LSTMs can pick out the subtle sentiments found within reviews thereby helping the system understand the nuances behind customer experiences, spot particular pain points as well as identify areas of satisfaction.



#### Disadvantages

  * Computational Intensity  
LSTMs’ computational complexity in training is high, particularly when dealing with large datasets leading to resource requirements. However, this can be challenging in terms of time and hardware.
  * Long Training Times  
Deep architecture causes longer training times for LSTMs than simpler models. Although this has been widely recognized as a problem, the benefit of the model’s ability to learn complex patterns is often seen to outweigh its costs.



### **Real Case Study of Recurrent Neural Networks (RNN)**

#### **Time Series Prediction for Financial Markets: Predicting Stock Prices in Finance**

#### Advantages

  * RNNs for Temporal Dependencies  
Recurrent neural networks, which are designed to capture temporal dependencies, are a natural fit for time series prediction tasks. In this finance-focused case study, an investment firm used RNNs to analyse past stock prices and predict future price trends based on the sequential nature of financial data.
  * Ability to Handle Sequences  
As sequences are inherent in RNN systems, they work best in tasks where ordering is necessary. This capability is very useful, particularly in financial arenas where historical trends significantly shape future predictions.



#### Disadvantages

  * Vanishing Gradient Problem  
The problem of vanishing gradient in RNN is important for understanding the long-term dependency model. Even though it can be mitigated to some extent, it still poses a problem for tasks demanding long-term context.
  * Limited Parallelization  
RNN's sequential nature limits their parallelization during training, which impacts scalability. It may be a drawback when working with large amounts of data.



### **Transformers**

#### **Revolutionising Chatbots in Customer Service**

#### Advantages

  * Efficient Parallelization  
The Transformer’s ability to parallelize greatly improves its efficiency when training large-scale models. For example, Transformers were integrated into a customer service platform used to build chatbots that understand and generate richly contextual messages which considerably reduced response time.
  * Contextual Understanding  
Transformers use a self-attention mechanism that allows them to encompass fine-grained contextual relationships in dialogues. This makes the chatbot able to offer more accurate and contextually relevant answers hence enhancing the overall consumer experience.



#### Disadvantages

  * Model Size and Resource Intensity  
Big Transformer models such as GPT-3 can be associated with huge numbers of parameters that demand substantial computational resources. Consequently, deploying these models could be challenging for resource-constrained applications.
  * Limited Interpretability  
Deep learning models including Transformers suffer from a black-box trait hence making it difficult for them to be interpreted. For instance, it is difficult to tell how the model came to those conclusions in sensitive domains which may raise concerns.



## **Future Trends in Text Analysis**

With the advancing technology, text analytics in the future is expected to have exciting developments and transformative trends. This part discusses what will shape the field of natural language processing in the coming years. 

  * Multimodal Integration  
The future of text analysis will not limit itself to traditional written languages. For instance, multimodal integration which combines textual information with other modalities like images, audio, and video may become a norm. These sophisticated models that can analyze and interpret information from various sources will enable a more comprehensive understanding of context on the one hand and increase the depth of insights drawn on the other.
  * Continued Advancements in Transformers  
Transformers have already revolutionized NLP, and further research is likely to produce even more effective and efficient ones. However, future transformer models could concentrate more on reducing computational requirements while maintaining or even enhancing performance. Additionally, improvements in dealing with long-range dependencies and context within text will be expected and challenges pertaining to capturing information can be addressed across extended sequences to improve the capabilities of upcoming transformer models.
  * AI in Text Analysis  
In the world of decision-making, AI systems are becoming more and more important, which is causing more attention to be paid to explainability. The next trends in text analysis will focus on providing models that give transparent and understandable insights. These developments are important as they help ensure that there is no unfair treatment or distrust when it comes to AI applications.



## **Challenges and Ethical Considerations of Text Analysis**

Text analysis evolution gives many prospects, but, at the same time, it has its challenges and ethical questions that must be approached with caution. This part deals with barriers and ethics about natural language processing developments.

  * Bias and Fairness  
Bias in training data is one of the main problems in text analysis. This challenge can only be handled through creating diverse and inclusive datasets as well as the adoption of fairness-aware algorithms. On the other hand, it is important to address bias transparently and ensure that there is fairness in the outcome of text analytics applications.
  * Privacy Concerns  
Text analysis often involves the use of personal or sensitive data. The ethical consideration here lies in finding a compromise between extracting valuable insights and respecting users’ privacy. Future advances have to emphasise privacy-preserving techniques and secure data anonymization so that unanticipated consequences are prevented.
  * Explainability and Accountability  
Making sure these text analytic models stay explainable is crucial as they become more complex. Users and stakeholders should be able to understand how decisions were made and why certain outcomes were predicted. In addition, striving for accountability in AI systems requires sharing information about their limitations and potential biases.
  * Security Risks  
Various applications that integrate text analysis introduce promising opportunities for security risks. Adversarial attacks are a hurdle where malicious actors try to manipulate or fool AI models. Failure to design strong protections against this kind of assault is essential in upholding the integrity and dependability of text analysis systems.
  * Accessibility and Digital Divide  
However, while advanced text analysis models are very powerful, making sure that they are accessible to all is still problematic. The digital divide needs addressing since some communities do not have access to modern technologies. Ethical grounds require equitable distribution of the advantages of text analysis without making existing societal gaps even wider.



## **Let’s Conclude**

In the journey through the worlds of text analysis, we have seen the transformation from traditional methods to more powerful ones – Long-short term memory (LSTM) and recurrent neural networks (RNN) in natural language processing (NLP) and transformers. These technologies are difficult to forget since they have shaped our understanding of how complex languages can be analysed.  
  
The impact of text analysis on different industries cannot be overemphasised when considering how sentiment analysis affects customer experiences, how market trends can be predicted, and how conversational AI revolutionises customer support thereby changing our interaction with information.  
  
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## 13 natural language processing algorithms

JAN. 14, 2025

6 Min Read

by

Lumenalta

Natural language processing algorithms shape how businesses automate, analyze, and enhance human-to-machine communication.

From text classification to sentiment analysis, these algorithms process and interpret vast amounts of language data, improving customer interactions, streamlining operations, and extracting critical insights. As AI adoption accelerates, choosing the right [NLP method](/services/ai-ml-llm/natural-language-processing-services) directly affects efficiency, accuracy, and business impact. Advancements in NLP have enabled organizations to scale language-based automation across industries, reducing costs while increasing the precision of text-based predictions. Businesses that apply the right algorithms can improve chatbot performance, optimize search functionalities, and refine document analysis. Understanding the differences between NLP approaches ensures companies can make informed choices that maximize operational value and long-term scalability.

Key takeaways

  * 1\. Natural language processing algorithms automate text-based tasks such as sentiment analysis, entity recognition, and machine translation.

  * 2\. Rule-based, machine learning-based, and deep learning approaches offer varying levels of accuracy, scalability, and interpretability for text processing.

  * 3\. Transformer networks have redefined NLP accuracy, improving AI-led automation and enhancing human-computer interactions.

  * 4\. Businesses apply NLP to chatbots, content recommendations, document analysis, and customer feedback processing for improved efficiency.

  * 5\. Selecting the right NLP algorithm optimizes text analytics, reduces operational costs, and strengthens AI-powered evaluation.




## Importance of natural language processing algorithms in 2025

[Natural language processing](/insights/what-is-natural-language-processing-nlp-updated-2025) (NLP) algorithms are essential for improving how machines interpret and generate human language. As [artificial intelligence](/insights/what-is-ai-an-overview-of-artificial-intelligence) (AI) adoption accelerates, businesses across industries are integrating NLP to automate workflows, enhance evaluation, and personalize user experiences. With advancements in [deep learning](/insights/what-is-deep-learning) and generative AI, the ability to process complex language structures with greater accuracy is becoming a standard requirement.

In 2025, NLP algorithms are expected to play a key role in refining conversational AI, improving multilingual capabilities, and enhancing text-based analytics. Businesses in [healthcare](/insights/nlp-in-healthcare), [finance](/insights/7-applications-of-nlp-in-finance), and [customer service](/insights/10-use-cases-of-nlp-in-customer-service) rely on these models to extract insights from unstructured data, streamline operations, and improve response accuracy. As algorithms become more sophisticated, they contribute to cost-effective automation, improved efficiency, and deeper contextual understanding—making them indispensable for organizations seeking long-term growth.

_" Selecting the right NLP method can affect efficiency, accuracy, and cost-effectiveness. The primary categories of NLP algorithms include symbolic, statistical, and hybrid approaches, each with distinct advantages and applications." _

## Natural language processing algorithm types

![](/_next/image?url=https%3A%2F%2Fa-us.storyblok.com%2Ff%2F1019928%2F735x437%2F40f4381c6a%2Fsymbolic_statistical_hybrid_nlp_algorithms.png%2Fm%2F&w=1920&q=75)

Algorithms for natural language processing are essential for businesses seeking to automate processes, extract valuable insights, and improve human-computer interactions. As companies integrate artificial intelligence into core operations, selecting the right NLP method can affect efficiency, accuracy, and cost-effectiveness. The primary categories of NLP algorithms include symbolic, statistical, and hybrid approaches, each with distinct advantages and applications. These methods determine how text is analyzed, structured, and interpreted, impacting everything from search engines to customer service automation.

### Symbolic algorithms

Symbolic NLP algorithms rely on predefined linguistic rules and structured frameworks to process text. These methods use dictionaries, syntax trees, and grammar rules to analyze language based on strict logical constructs. Businesses that require high precision, such as legal or regulatory compliance teams, often apply symbolic models to maintain consistency in language interpretation. While effective in controlled applications, symbolic algorithms require ongoing manual updates to accommodate new language patterns and may struggle with ambiguous or informal text.

### Statistical algorithms

Statistical NLP algorithms apply mathematical models to analyze text based on probability distributions and data-backed patterns. These methods assess word frequencies, sequence structures, and contextual relationships to make predictions about language usage. Applications such as sentiment analysis, automatic translation, and text summarization rely on statistical models to process vast amounts of unstructured data with scalable efficiency. While statistical methods improve accuracy as datasets expand, their performance depends on data quality and requires substantial computational power for optimal results.

### Hybrid algorithms

Hybrid NLP algorithms combine symbolic rule-based structures with statistical learning models to enhance accuracy and adaptability. This approach applies linguistic rules to guide text interpretation while allowing [machine learning](/insights/ai-and-machine-learning-ml) techniques to refine predictions through pattern recognition. Hybrid models are frequently used in speech recognition, chatbots, and content moderation, where structured rules improve reliability while data-led refinements enhance flexibility. Organizations benefit from this method when aiming to balance consistency with the ability to process developing language structures.

Choosing the right NLP algorithm affects processing speed, operational costs, and long-term scalability. Symbolic models provide structured control, statistical approaches offer scalability, and hybrid methods create a balance between precision and adaptability. Businesses that incorporate the appropriate NLP techniques can optimize automation, improve language understanding, and increase the accuracy of AI-powered interactions.

## Categories of natural language processing algorithms

![](/_next/image?url=https%3A%2F%2Fa-us.storyblok.com%2Ff%2F1019928%2F735x437%2Fc08f67a250%2Fnlp_ai_automation_machine_learning.png%2Fm%2F&w=1920&q=75)

Natural language processing algorithms can be grouped into categories based on how they interpret, process, and analyze text. These categories determine the methods for extracting meaning from language, affecting accuracy, scalability, and adaptability in different applications. Businesses rely on these methods to automate customer interactions, improve content recommendations, and extract structured insights from unstructured data. The three primary categories include rule-based, machine learning-based, and profound learning approaches, each offering distinct advantages for specific use cases.

### Rule-based methods

Rule-based NLP methods rely on manually defined linguistic rules to process and analyze text. These systems use lexicons, grammar rules, and pattern-matching techniques to extract structured information. Rule-based approaches are commonly applied in applications requiring precision, such as legal text analysis and medical coding. While effective in structured conditions, these methods require continuous updates to accommodate new language variations and may struggle with ambiguous or context-dependent text.

### Machine learning-based methods

[Machine learning](/insights/13-benefits-and-challenges-of-machine-learning)-based NLP methods use statistical models to identify patterns in large datasets and improve language understanding. These approaches train on labeled data to recognize relationships between words, sentences, and contextual elements. Applications such as spam filtering, sentiment analysis, and automated text classification rely on machine learning methods to make accurate predictions. While these models reduce manual rule creation, they require high-quality training data and computational resources for effective implementation.

### Deep learning approaches

[Deep learning](/insights/deep-learning-vs-machine-learning) approaches use advanced neural networks to process language with greater accuracy and complexity. These methods analyze large datasets to recognize intricate patterns, improving tasks such as machine translation, speech recognition, and chatbots. Deep learning techniques, such as transformer and recurrent neural networks, enhance contextual understanding and generate more human-like responses. While offering high accuracy and adaptability, deep learning models require significant computational power and extensive datasets to train effectively.

Each NLP category offers unique benefits based on business objectives and data availability. Rule-based methods provide structured control, machine learning approaches enhance pattern recognition, and deep learning models deliver high-accuracy language processing. Selecting the right approach can improve automation efficiency, optimize resource allocation, and strengthen text-based analytics in enterprise applications.

## 13 natural language processing algorithms

![](/_next/image?url=https%3A%2F%2Fa-us.storyblok.com%2Ff%2F1019928%2F735x437%2Fab47dbe043%2Ftransformer_networks_nlp_ai_text_analysis.png%2Fm%2F&w=1920&q=75)

Selecting the right algorithms for natural language processing affects how well [AI-powered systems](/services/ai-ml-llm/artificial-intelligence-ai-solutions) interpret, analyze, and generate human language. Businesses rely on these models to automate customer interactions, extract insights from text data, and improve content recommendations. The following NLP algorithms provide various solutions for topics modeling, text classification, sentiment analysis, and entity recognition.

### 1\. Latent Dirichlet Allocation (LDA)

Latent Dirichlet Allocation is a statistical model for topic modeling, identifying hidden themes in large text datasets. This algorithm assigns words to multiple topics based on probability distributions, making it helpful in organizing documents, summarizing content, and improving search functionalities. Businesses apply LDA in applications such as market research, content categorization, and automated tagging systems.

LDA helps companies process vast amounts of unstructured text, improving knowledge management and content retrieval. Organizations that need to analyze customer feedback, online reviews, or research papers can use this method to extract meaningful patterns and enhance evaluation accuracy.

### 2\. Conditional Random Fields (CRF)

Conditional Random Fields is a [machine learning model](/services/ai-ml-llm/ml-model-development-services) for sequence prediction tasks such as named entity recognition and part-of-speech tagging. Unlike traditional classification methods, CRF considers contextual relationships between words, improving accuracy in structured text processing.

Industries such as healthcare and finance use CRF to extract key entities from documents, ensuring compliance and improving data classification. Businesses benefit from this algorithm when developing automated document processing systems, chatbots, and virtual assistants.

### 3\. Hidden Markov Model (HMM)

Hidden Markov Model is a probabilistic model that analyzes sequences of observations, making it effective for speech recognition, text generation, and sentiment analysis. This algorithm estimates the likelihood of a word sequence based on previous states, improving contextual predictions.

Organizations use HMM in applications such as automated transcription services, language modeling, and financial market analysis. The ability to process sequential data with statistical accuracy makes HMM a foundational component in many NLP-based automation tools.

### 4\. Naive Bayes

Naive Bayes is a probabilistic classifier that determines the likelihood of a word or phrase belonging to a specific category. This algorithm is widely used in spam detection, sentiment analysis, and text classification due to its simplicity and efficiency.

Businesses rely on Naive Bayes to automate email filtering, categorize customer reviews, and detect fraudulent activities. While effective for many NLP tasks, its performance depends on assumptions of word independence, making it less suitable for highly context-dependent applications.

### 5\. Support Vector Machines (SVM)

Support Vector Machines is a supervised learning model for text classification, sentiment analysis, and document categorization. This algorithm identifies decision boundaries that separate different text classes, improving classification accuracy.

Companies apply SVM to customer service automation, social media monitoring, and risk analysis. Its ability to handle high-dimensional data makes it a reliable choice for NLP applications that require precise text categorization and predictive modeling.

### 6\. Recurrent Neural Networks (RNN)

Recurrent Neural Networks process sequential data by maintaining memory of previous inputs, making them ideal for tasks such as [language modeling](/insights/7-surprisingly-powerful-large-language-model-applications), text generation, and machine translation. RNNs capture dependencies between words, improving the accuracy of text-based predictions.

Organizations use RNNs for chatbot development, predictive text input, and automated report generation. While powerful, traditional RNNs struggle with long-term dependencies, leading to the development of more advanced variations such as Long Short-Term Memory (LSTM) networks.

### 7\. Convolutional Neural Networks (CNN)

Convolutional Neural Networks, commonly associated with image processing, are also applied to NLP tasks such as text classification, sentiment analysis, and spam detection. CNNs extract key features from text using convolutional layers, improving classification accuracy.

Businesses leverage CNNs to analyze customer feedback, detect fake reviews, and optimize search engine results. Their ability to recognize complex text patterns makes them effective for sentiment-based analytics and content moderation.

### 8\. Transformer Networks

Transformer networks revolutionized NLP by introducing self-attention mechanisms that improve contextual understanding. These models, including architectures such as BERT and [GPT](https://chatgpt.com/), excel in machine translation, text summarization, and conversational AI.

Enterprises use transformers to enhance chatbots, automate customer support, and generate high-quality content. Their ability to process long-range dependencies and contextual relationships makes them critical in modern NLP applications.

### 9\. Word2Vec

Word2Vec is an NLP algorithm that converts words into dense vector representations, capturing semantic relationships between terms. This method improves search recommendations, keyword extraction, and text similarity analysis.

Businesses apply Word2Vec in content [personalization](/insights/what-is-personalization), recommendation engines, and knowledge graph development. Its ability to enhance contextual text matching makes it valuable for applications requiring precise language understanding.

### 10\. Bag of Words (BoW)

Bag of Words is a simple NLP technique that represents text as a collection of word occurrences without considering grammar or word order. This approach is widely used in sentiment analysis, text classification, and keyword extraction.

Companies rely on BoW for document indexing, spam detection, and automated tagging. While easy to implement, BoW struggles with understanding contextual meaning, requiring complementary methods for improved accuracy.

### 11\. TF-IDF (Term Frequency-Inverse Document Frequency)

TF-IDF measures word importance within a document relative to a larger corpus, improving information retrieval and keyword extraction. Search engines and text analytics platforms use this method to enhance content ranking and topic identification.

Businesses apply TF-IDF in search engine optimization, document retrieval, and content categorization. This algorithm improves text relevance scoring, optimizing search functionalities across various industries.

### 12\. Named Entity Recognition (NER)

Named Entity Recognition identifies specific entities such as names, locations, and dates within text, improving information extraction accuracy. NER is widely used in customer support automation, knowledge management, and document analysis.

Organizations apply NER to improve chatbot responses, automate legal document processing, and enhance financial data extraction. Its ability to structure unstructured text makes it essential for data-based [decision-making](/insights/how-ai-decision-making-improves-business-outcomes).

### 13\. Sentiment Analysis

Sentiment analysis determines the emotional tone of the text, classifying it as positive, negative, or neutral. This method helps businesses analyze customer opinions, monitor brand perception, and optimize content strategies.

Companies use sentiment analysis for social media monitoring, customer feedback analysis, and market research. Understanding user sentiment allows organizations to refine engagement strategies and improve user experiences.

NLP algorithms provide valuable solutions for automating text analysis, improving content understanding, and extracting meaningful insights from language data. Selecting the right algorithm depends on business needs, available data, and performance requirements. Organizations that integrate these methods into their [AI strategies](/insights/how-to-invest-in-ai-key-strategies-for-success) can improve operational efficiency, reduce manual processing costs, and enhance human-computer interactions.

## Evolution of natural language processing algorithms

![](/_next/image?url=https%3A%2F%2Fa-us.storyblok.com%2Ff%2F1019928%2F735x437%2F5965027abe%2Fevolution_nlp_algorithms_ai_progress.png%2Fm%2F&w=1920&q=75)

Natural language processing algorithms have advanced significantly, moving from rule-based systems to deep learning models capable of understanding complex language patterns. Early methods relied on manually defined rules and structured grammar models, limiting adaptability and requiring extensive human intervention. These symbolic approaches worked well in structured settings but struggled with informal or ambiguous text.

The introduction of statistical models improved NLP by incorporating probability-based predictions, allowing algorithms to learn from large datasets. Methods such as Naive Bayes and Hidden Markov Models brought automation to text classification, speech recognition, and sentiment analysis. As computing power increased, machine learning methods further enhanced NLP by training models on vast amounts of textual data, reducing the need for manual rule engineering.

Deep learning reformed NLP by introducing neural networks capable of capturing intricate language relationships. Transformer-based models, such as BERT and GPT, set new standards for contextual understanding, granting AI to generate human-like text, improve translations, and refine chat-based interactions. These advancements have increased automation accuracy, reduced processing times, and expanded NLP applications across industries. Businesses that incorporate modern NLP techniques can optimize customer interactions, extract deeper insights from text, and improve AI-led language understanding.

_" Deep learning transformed NLP by introducing neural networks capable of capturing intricate language relationships. Transformer-based models, such as BERT and GPT, set new standards for contextual understanding, allowing AI to generate human-like text, improve translations, and refine chat-based interactions." _

## Choosing the right natural language processing algorithm

![](/_next/image?url=https%3A%2F%2Fa-us.storyblok.com%2Ff%2F1019928%2F735x437%2F1d18b150e5%2Fbusiness_nlp_chatbot_automation.png%2Fm%2F&w=1920&q=75)

Selecting the most effective natural language processing algorithm depends on the complexity of the task, available data, and required accuracy. Businesses must consider whether they need a rule-based, machine learning, or deep learning approach to balance interpretability, scalability, and performance.

For structured text processing, rule-based methods offer precision and control, making them ideal for regulatory compliance, grammar checking, and predefined language tasks. These approaches work best when language patterns are stable and do not require extensive contextual adaptation. However, they struggle with flexible language structures and require ongoing manual updates.

Machine learning-based NLP models improve automation by recognizing text patterns and classifying data with minimal human intervention. Algorithms such as Naive Bayes, support vector machines, and recurrent neural networks provide [scalable solutions](/insights/what-is-scalable-ai) for tasks such as sentiment analysis, spam filtering, and document categorization. These models can benefit businesses that need accurate text classification without complex training requirements.

Deep learning approaches, including transformer networks and convolutional neural networks, provide high accuracy for complex NLP tasks such as machine translation, [chatbots](/insights/chatbots-vs-conversational-ai:-key-differences-explained), and voice recognition. These models process vast amounts of text data to improve contextual understanding, delivering more natural and human-like responses. While highly effective, deep learning models require significant computational resources and large datasets to train effectively.

Choosing the right NLP algorithm affects processing efficiency, response accuracy, and operational costs. Businesses that align NLP methods with their specific requirements can improve automation, enhance language interpretation, and extract valuable insights from text data, strengthening AI-led interactions across various applications.

[Natural language processing](/services/ai-ml-llm/natural-language-processing-services) reshapes businesses' interactions with data, customers, and automation. With the right algorithms, companies can achieve greater efficiency, improve language comprehension, and unlock valuable insights from text. At [Lumenalta](/home), we develop tailored AI solutions to enhance NLP capabilities while aligning with your strategic objectives. Let’s build more competent, more scalable automation together.

Table of contents

  * Importance of natural language processing algorithms in 2025
  * Natural language processing algorithm types
  * Categories of natural language processing algorithms
  * 13 natural language processing algorithms
  * 1\. Latent Dirichlet Allocation (LDA)
  * 2\. Conditional Random Fields (CRF)
  * 3\. Hidden Markov Model (HMM)
  * 4\. Naive Bayes
  * 5\. Support Vector Machines (SVM)
  * 6\. Recurrent Neural Networks (RNN)
  * 7\. Convolutional Neural Networks (CNN)
  * 8\. Transformer Networks
  * 9\. Word2Vec
  * 10\. Bag of Words (BoW)
  * 11\. TF-IDF (Term Frequency-Inverse Document Frequency)
  * 12\. Named Entity Recognition (NER)
  * 13\. Sentiment Analysis
  * Evolution of natural language processing algorithms
  * Choosing the right natural language processing algorithm
  * Common questions about NLP algorithms



## Common questions about NLP algorithms

### What are natural language processing algorithms used for?

### How do NLP algorithms improve AI-backed automation?

### What is the difference between rule-based and machine learning NLP methods?

### Which NLP algorithm is best for sentiment analysis?

### How do transformer networks improve NLP accuracy?

Want to learn how  NLP  can bring more transparency and trust to your operations?

[Get started](/services/ai-ml-llm/natural-language-processing-services)

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Endocrinol Metab (Seoul)

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# A Review of the Effects of Glucagon-Like Peptide-1 Receptor Agonists and Sodium-Glucose Cotransporter 2 Inhibitors on Lean Body Mass in Humans

[Jack Alistair Sargeant](https://pubmed.ncbi.nlm.nih.gov/?term=%22Sargeant%20JA%22%5BAuthor%5D)

### Jack Alistair Sargeant

1Diabetes Research Centre, University of Leicester, Leicester, UK.

2NIHR Leicester Biomedical Research Centre, University Hospital of Leicester NHS Trust and the University of Leicester, Leicester, UK.

Find articles by [Jack Alistair Sargeant](https://pubmed.ncbi.nlm.nih.gov/?term=%22Sargeant%20JA%22%5BAuthor%5D)

1,2,*, [Joseph Henson](https://pubmed.ncbi.nlm.nih.gov/?term=%22Henson%20J%22%5BAuthor%5D)

### Joseph Henson

1Diabetes Research Centre, University of Leicester, Leicester, UK.

2NIHR Leicester Biomedical Research Centre, University Hospital of Leicester NHS Trust and the University of Leicester, Leicester, UK.

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1,2,*, [James Adam King](https://pubmed.ncbi.nlm.nih.gov/?term=%22King%20JA%22%5BAuthor%5D)

### James Adam King

2NIHR Leicester Biomedical Research Centre, University Hospital of Leicester NHS Trust and the University of Leicester, Leicester, UK.

3National Centre for Sport and Exercise Medicine, Loughborough University, Loughborough, UK.

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2,3, [Thomas Yates](https://pubmed.ncbi.nlm.nih.gov/?term=%22Yates%20T%22%5BAuthor%5D)

### Thomas Yates

1Diabetes Research Centre, University of Leicester, Leicester, UK.

2NIHR Leicester Biomedical Research Centre, University Hospital of Leicester NHS Trust and the University of Leicester, Leicester, UK.

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1,2, [Kamlesh Khunti](https://pubmed.ncbi.nlm.nih.gov/?term=%22Khunti%20K%22%5BAuthor%5D)

### Kamlesh Khunti

1Diabetes Research Centre, University of Leicester, Leicester, UK.

4NIHR Collaborations for Leadership in Applied Health Research and Care (CLAHRC), Leicester, UK.

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1,4, [Melanie Jane Davies](https://pubmed.ncbi.nlm.nih.gov/?term=%22Davies%20MJ%22%5BAuthor%5D)

### Melanie Jane Davies

1Diabetes Research Centre, University of Leicester, Leicester, UK.

2NIHR Leicester Biomedical Research Centre, University Hospital of Leicester NHS Trust and the University of Leicester, Leicester, UK.

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1,2,✉

  * Author information
  * Article notes
  * Copyright and License information



1Diabetes Research Centre, University of Leicester, Leicester, UK.

2NIHR Leicester Biomedical Research Centre, University Hospital of Leicester NHS Trust and the University of Leicester, Leicester, UK.

3National Centre for Sport and Exercise Medicine, Loughborough University, Loughborough, UK.

4NIHR Collaborations for Leadership in Applied Health Research and Care (CLAHRC), Leicester, UK.

✉

Corresponding author: Melanie Jane Davies. Diabetes Research Centre, Leicester General Hospital, Gwendolen Rd, Leicester, LE5 4PW, UK. Tel: +44-116-258-6481, melanie.davies@uhl-tr.nhs.uk

*These authors contributed equally to this work.

✉

Corresponding author.

Received 2019 Sep 3; Revised 2019 Sep 10; Accepted 2019 Sep 17; Issue date 2019 Sep.

Copyright © 2019 Korean Endocrine Society

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (<http://creativecommons.org/licenses/by-nc/4.0/>) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

[PMC Copyright notice](/about/copyright/)

PMCID: PMC6769337 PMID: [31565876](https://pubmed.ncbi.nlm.nih.gov/31565876/)

## Abstract

Weight loss is an important goal in the management of several chronic conditions, including type 2 diabetes mellitus, and pharmacological therapies that aid weight loss are appealing. Glucagon-like peptide-1 receptor agonists (GLP-1RAs) and sodium-glucose cotransporter 2 inhibitors (SGLT2is) are novel glucose-lowering therapies that have been shown to induce clinically significant reductions in body weight. However, this weight loss may not be attributed solely to fat mass (FM). Given the importance of skeletal muscle and lean body mass (LBM) on cardio-metabolic health and physical function, we reviewed the available literature reporting the effects of GLP-1RAs and SGLT2is on body composition. Results demonstrate that, in most circumstances, the weight loss associated with both therapies predominantly comprises a reduction in FM, although significant heterogeneity exists between studies. In over half of the studies identified, the proportion of LBM reduction ranged between 20% and 50% of total weight lost, which is consistent with diet-induced weight loss and bariatric surgery. No clear differences existed between GLP-1RAs and SGLT2is. Consequently, the loss of LBM and skeletal muscle associated with weight loss induced by GLP-1RAs and SGLT2is warrants attention. Strategies to preserve skeletal muscle and improve physical function, for example through structured exercise, are of great importance.

**Keywords:** Glucagon-like peptide-1 receptor agonists; Sodium-glucose cotransporter 2 inhibitors; Diabetes mellitus, type 2; Weight loss; Body composition; Lean body mass

## INTRODUCTION

Glucagon-like peptide-1 receptor agonists (GLP-1RAs) and sodium-glucose cotransporter 2 inhibitors (SGLT2is) are novel glucose-lowering therapies that are prominent in the most recent guidelines for the management of hyperglycaemia in type 2 diabetes mellitus (T2DM) and recommended as preferred second-line pharmacological therapies after metformin, alongside ongoing lifestyle management (predominantly diet and physical activity) [1]. They are particularly recommended when, in addition to glucose lowering, there is a compelling need to reduce body weight (BW) [1].

In large cardiovascular outcome trials, several GLP-1RAs (liraglutide, semaglutide, exenatide once-weekly, albiglutide and dulaglutide) have shown compelling cardiovascular protection in the form of reduced risk of major adverse cardiovascular events, along with some renal benefits [2,3]. A number of SGLT2is (canagliflozin, dapagliflozin, and empagliflozin) have also shown cardiovascular benefits and greater renal protection, in addition to reduced hospitalization for heart failure [4,5,6]. Both classes of therapy also elicit clinically relevant weight loss [1], with liraglutide gaining an independent license (at a higher dose of 3 mg) for use in obesity management. Importantly, the glucose-dependent mechanisms of action of both therapies mean that their beneficial effects come with low risk of hypoglycaemia, particularly when not used with sulphonylureas or insulin [1].

The dual effects of weight loss and improved glycaemic control associated with GLP-1RAs and SGLT2is are appealing. Obesity and T2DM are intrinsically linked, and each are associated with increased risk of multiple comorbidities [7]. Notably, however, previous studies demonstrate that when BW is reduced through dietary energy restriction, not all of the resulting weight loss can be attributed to fat mass (FM), with approximately 25% to 33% estimated to comprise of reductions in lean body mass (LBM) [8,9]. This is important because LBM (predominantly comprised of skeletal muscle) has several important functions. It acts as a primary site of glucose disposal (with lower skeletal muscle mass contributing to poorer glycaemic control [10]), and is a strong determinant of resting metabolic rate; and thus loss of skeletal muscle with weight loss may predispose individuals to a greater chance of weight regain [11].

Lower muscle mass and function, associated with impaired muscular strength and endurance, also result in a higher risk of falls, hospitalisation and physical frailty [12,13]. The important association between T2DM and frailty is becoming increasingly recognised [14], with frailty up to five times more likely in individuals with T2DM compared to those without [14,15,16,17]. T2DM represents a state of accelerated metabolic ageing, and some of this frailty risk may be underpinned by an increased loss of LBM and function [18,19,20].

Consequently, whilst weight loss is an important goal in the management of several obesity-associated comorbidities, including T2DM, and pharmacological therapies that support such weight loss are appealing, it is important to understand the impact of these therapies on body composition. The aim of this narrative review is to describe the effects of GLP-1RAs and SGLT2is upon body composition, with a particular focus on LBM and skeletal muscle.

## DEFINITIONS AND MEASUREMENT OF BODY COMPOSITION

To interpret changes in body composition, it is important to understand the basic theory and appreciate the advantages and disadvantages of different measurement methods.

Broadly, body composition measurement divides the body into “compartments” on the basis of differing physical properties, which commonly include FM, fat free mass (FFM), LBM, skeletal muscle mass, bone mineral content and total body water (TBW). Definitions of these terms are provided in Table 1.

### Table 1. Definitions of Body Composition Compartments.

Fat mass | Mass of all adipose tissue  
---|---  
Fat free mass | Fat free mass is total body mass minus total fat mass  
Lean body mass | Lean body mass is fat free mass minus total bone mass  
Skeletal muscle mass | Skeletal muscle mass is lean body mass minus connective tissue, skin, and other organs  
Total body water | The summation of intra- and extra-cellular water  
  
[Open in a new tab](table/T1/)

Direct methods of body composition include computed tomography (CT) and magnetic resonance imaging (MRI) [21]. Whilst these techniques have the highest accuracy, they are also expensive, and not widely used in clinical practice. More common measures, which estimate body composition indirectly, include bioelectrical impedance analysis (BIA), air displacement plethysmography (ADP) and dual-energy X-ray absorptiometry (DXA).

BIA uses a small alternating current to measure body impedance. Estimates of body composition are provided through in-built equations based on assumed impedance of different biological tissues [22]. BIA is quick, easy and relatively inexpensive, but less precise than other available methods and influenced by factors such as hydration status.

ADP provides an estimate of body composition by combining body volume, measured using the displacement of air within a sealed measurement chamber, with BW to calculate body density. Body density is then used to estimate body composition using pre-defined formulae [23]. ADP is relatively quick to perform and non-invasive, but requires operation by trained personnel to avoid undue error.

DXA remains a prominent and preferred technique in clinical trials, balancing a high level of accuracy with comparatively lower costs than MRI and CT. DXA provides whole-body and regional estimates of FM, FFM, LBM, and bone mineral content [21]; using a small safe dose of radiation. It is relatively quick and provides much greater accuracy than BIA.

## METHODS

We searched for published studies reporting data on the effects of GLP-1RAs and SGLT2is on body composition using the online database, PubMed, from inception to August 2019, using search terms related to GLP-1RAs, SGLT2is, weight loss and body composition. We included studies using DXA, BIA, ADP, MRI, or CT to measure body composition in any human study population before and after 2 or more weeks of GLP-1RA and/or SGLT2i therapy. Where appropriate, the following assumptions were used during data extraction:

(1) Where data were reported for mean total BW and mean body fat percentage, we estimated mean FM as; BW divided by 100, multiplied by body fat percentage.

(2) Where mean BW and mean FM were available (including when FM was estimated as above) we estimated FFM as; BW minus FM.

We used FFM to cautiously infer changes in LBM, with recognition of their subtle differences as outlined in Table 1. Due to variation in techniques used and methods of reporting, we did not sum different body compartments to estimate total LBM, FFM or BW change.

## RESULTS

Details of all eligible studies identified in our search can be found in Tables 2,3,4 [24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68]. The following paragraphs provide an overview of the findings for each individual therapy within both drug classes. For ease of reading, medication doses are not included in text unless providing specific context or comparison. Figs. 1, 2 present the relative proportion of weight loss that could be attributed to FM and LBM/FFM with GLP-1RA and SGLT2i therapy, respectively. Our literature search yielded no data for lixisenatide, albiglutide, or ertugliflozin.

### Table 2. Studies Reporting Changes in Body Composition with GLP-1RA Therapy.

Study (location) | Study design | No. prescribed GLP-1RA therapy | Population | Dose (all subcutaneous injection) | Duration, wk | Tech. used | Total BW change, kg (% change from baseline) | FM change, kg | LBM change, kg | Skeletal muscle mass change, kg | Total body water change, kg | Proportion of weight loss from FM, % | Proportion of weight loss from LBM, %  
---|---|---|---|---|---|---|---|---|---|---|---|---|---  
Liraglutide |  |  |  |  |  |  |  |  |  |  |  |  |   
Astrup et al. (2012) [24] (Europe; multi-site) | Six-arm RCT (sub-study) | 15 | Obesity | 1.2 mg once-daily | 20 | DXA | NR | −6.1 | −0.5 | NR | NR | - | -  
13 | 1.8 mg once-daily | NR | −5.9 | −1.5 | NR | NR | - | -  
15 | 2.4 mg once-daily | NR | −7.0 | −1.3 | NR | NR | - | -  
15 | 3.0 mg once-daily | NR | −6.8 | −1.1 | NR | NR | - | -  
Feng et al. (2019) [25] (China) | Three-arm RCT | 29 | T2DM and NAFLD | 1.8 mg once-daily (titrated from 0.6 mg as tolerated) | 24 | DXA | −5.6 (−7.4%) | −3.6 | −0.2 | NR | NR | 64.3 | 3.6  
Frossing et al. (2018) [26] (Denmark) | Two-arm RCT | 44 | PCOS | 1.8 mg once-daily | 26 | DXA | −5.2 (−5.5%) | −2.6 | −2.4 | NR | NR | 50 | 46.1  
Harder et al. (2004) [27] (Denmark) | Two-arm RCT | 21 | T2DM | 0.6 mg once-daily | 8 | DXA | −2.1 (−2.0%) | −1.6 | +0.6 | NR | NR | 76.2 | LBM increased  
Jendle et al., (2009) [28] (Global; multi-site) | Five-arm RCT Study 1; LEAD 2 (sub-study) | 35 | T2DM | 0.6 mg once-daily (with 1.5–2.0 g metformin) | 26 | DXA | −0.9 (−1.0%) | −0.7 | −0.3 | NR | NR | 77.8 | 33.3  
31 | 1.2 mg once-daily (with 1.5–2.0 g metformin) | −2.0 (−2.3%) | −1.6 | −0.8 | NR | NR | 80 | 40  
37 | 1.8 mg once-daily (with 1.5–2.0 g metformin) | −3.2 (−3.6%) | −2.4 | −1.5 | NR | NR | 75.0 | 46.9  
Three-arm RCT Study 2; LEAD 3 (sub-study) | 23 | T2DM | 1.2 mg once-daily | 52 | DXA | −2.4 (−2.6%) | −2.0 | −1.1 | NR | NR | 83.3 | 45.8  
20 | 1.8 mg once-daily | −2.3 (−2.5%) | −1.0 | −1.5 | NR | NR | 43.5 | 65.2  
Li et al. (2014) [29] (China) | Case series (prospective) | 31 | Obesity and T2DM | 1.2 mg once-daily (titrated from 0.6 mg as tolerated) | 12 | DXA | −5.0 (−5.5%) | −3.8 | −1.5 | NR | NR | 76.0 | 30.0  
Perna et al. (2016) [30] (Italy) | Case series (retrospective) | 9 | Obesity and T2DM (elderly) | 3 mg once-daily (titrated from 1.2 mg as tolerated) | 24 | DXA | −2.0 (−2.3%) | −1.5 | +0.1a | NR | NR | 75.0 | FFM increased  
Rondanelli et al. (2016) [31] (Italy) | Case series (prospective) | 28 | Overweight/ obese and T2DM | 3 mg once-daily (titrated from 1.2 mg as tolerated) | 24 | DXA | −2.5 (−2.6%) | −2.0 | −0.4a | NR | NR | 80.0 | 16.0  
Dube et al. (2018) [32] (Canada) | Two-arm RCT | 15 | T1DM | 1.8 mg once-daily (titrated from 0.6 mg as tolerated) | 24 | BIA/ CT | −5.6 (−6.3%) | −3.3 | −2.3a | NR | NR | 58.9 | 41.1  
Exenatide |  |  |  |  |  |  |  |  |  |  |  |  |   
Bradley et al. (2012) [33] (USA) | Case series (prospective) | 18 | Obesity | 10 μg twice-daily (titrated from 5 μg as tolerated) | 14 | DXA | −2.0 (−2.0%) | −1.3 | −0.8a | NR | NR | 65 | 40  
Bunck et al. (2010) [34] (Sweden) | Two-arm RCT | 29 | T2DM | 20 μg twice-daily (titrated from 5 μg as tolerated) | 52 | DXA | −3.9 (−4.3%) | −2.4 | +0.3 | NR | NR | 61.5 | LBM increased  
Ishoy et al. (2017) [35] (Denmark) | Two-arm RCT | 20 | Obesity and schizophrenia (treated with anti-psychotic medication) | 2 mg weekly | 14 | DXA | −2.3 (−1.9%) | −1.1 | −1.2a | −0.7 | NR | 47.8 | 52.2  
Yin et al. (2018) [36] (China) | Two-arm RCT | 19 | Overweight/ obesity and T2DM | 10 μg twice-daily (titrated from 5 μg as tolerated) | 16 | DXA | −3.5 (−4.3%) | −2.1 | −0.4 | NR | NR | 60 | 11.4  
Hong et al. (2016) [37] (Korea) | Case series (prospective) | 32 | Obesity and T2DM | 10 μg twice-daily (titrated from 5 μg as tolerated) | 12 | BIA | −1.0 (−1.4%) | −2.1 | NR | +1.3 | NR | 210 | -  
Semaglutide |  |  |  |  |  |  |  |  |  |  |  |  |   
Blundell et al. (2017) [38] (UK) | Randomised, placebo-controlled corss-over trial | 30 | Obesity | 1.0 mg (titrated up from 0.25 mg) | 12 | ADP | −5.0 (−4.9%) | −3.5 | −1.1 | NR | NR | 70.0 | 22.0  
Dulaglutide |  |  |  |  |  |  |  |  |  |  |  |  |   
Seko et al. (2017) [39] (Japan) | Case series (retrospective) | 5 | T2DM and NAFLD | 0.75 mg once-daily | 12 | BIA | NR | −1.9 | −0.1 | −0.2 | −0.1 | - | -  
  
[Open in a new tab](table/T2/)

GLP-1RA, glucagon-like peptide-1 receptor agonist; BW, body weight; FM, fat mass; LBM, lean body mass; RCT, randomised controlled trial; DXA, dual-energy X-ray absorptiometry; NR, not reported; T2DM, type 2 diabetes mellitus; NAFLD, nonalcoholic fatty liver disease; PCOS, polycystic ovarian syndrome; LEAD, Liraglutide Effect and Action in Diabetes phase 3 programme for liraglutide; FFM, fat-free mass; T1DM, type 1 diabetes mellitus; BIA, bioelectrical impedance analysis; CT, computed tomography; ADP, air displacement plethysmography.

aFFM.

### Table 3. Studies Reporting Changes in Body Composition with SGLT2i Therapy.

Study (location) | Study design | No. (in group prescribed SGLT2i therapy) | Population | Dose (all once-daily, oral tablet unless otherwise specified) | Duration | Tech. used | Total BW change, kg (% change from baseline) | FM change, kg | LBM change, kg | Skeletal muscle mass change, kg | Total body water change, kg | Proportion of weight loss from FM, % | Proportion of weight loss from LBM, %  
---|---|---|---|---|---|---|---|---|---|---|---|---|---  
Various |  |  |  |  |  |  |  |  |  |  |  |  |   
Arase et al. (2019) [64] (Japan) | Case series (retrospective) | 17 (DAPA=10; CANA=7) | Overweight, T2DM and NAFLD | DAPA: 5 mg | 24 wk | BIA | −5.2 (−7.1%) | −2.7 | −2.5a | −0.7 | −1.2 | 51.9 | 48.1  
CANA: 100 mg  
Kinoshita et al. (2018) [65] (Japan) | Case series (prospective) | 156 (_n_ =108 for body composition) (LUSEO=52%; TOFO=29%; IPRA=15%; DAPA=4%) | T2DM | NR | 12 wk | BIA | −2.8 (−3.6%) | NR | NR | −0.1 | NR | - | -  
Seko et al. (2017) [66] (Japan) | Case series (retrospective) | 24 (_n_ =17 for body composition) (CANA=18; IPRA=6) | T2DM and biopsy-proven NAFLD/NASH | CANA: 100 mg | 24 wk | BIA | NR | −2.4 | −1.1 | −0.6 | −0.8 | - | -  
IPRA: 50 mg  
Canagliflozin |  |  |  |  |  |  |  |  |  |  |  |  |   
Blonde et al. (2016) [40] Study 2 (Global; multi-site) | Three-arm RCT (sub-study) | CANA 100=63 | T2DM | 100 mg | 26 wk | DXA | −2.5 (−2.8%) | −1.9 | −0.9 | NR | NR | 76 | 36.0  
CANA 300=71 | 300 mg | −3.2 (−3.4%) | −2.4 | −1.2 | NR | NR | 75 | 37.5  
Cefalu et al. (2013) [41]b (Global; multi-site) | Three-arm RCT (sub-study) | CANA 100=111 | T2DM | 100 mg | 52 wk | DXA | −4.4 (−5.2%) | −2.9 | −0.9 | NR | NR | 65.9 | 20.5  
CANA 300=102 | 300 mg | −4.2 (−4.9%) | −2.5 | −1.1 | NR | NR | 59.5 | 26.2  
Koike et al. (2019) [42] (Japan) | Case series (prospective) | 38 | T2DM | 100 mg | 24 wk | DXA | −2.4 (−3.3%) | −1.3 | −1.1 | NR | NR | 54.2 | 45.8  
Inoue et al. (2019) [43] (Japan) | Case series (prospective) | 20 | T2DM and NAFLD | 100 mg | 12 mo | BIA | −2.9 (−3.5%) | −2.6 | −0.2 | −0.2 | −0.2 | 89.7 | 6.9  
Dapagliflozin |  |  |  |  |  |  |  |  |  |  |  |  |   
Bolinder et al. (2014) [44] (Europe; multi-site) | Two-arm RCT | 69 | Overweight/obese with T2DM | 10 mg (added to open-label metformin) | 104 wk | DXA | −4.5 (−4.9%) | −2.8 | −1.3 | NR | NR | 62.2 | 28.9  
Kosugi et al. (2019) [45] (Japan) | Case series (prospective) | 26 | T2DM | 5 mg | 12 wk | DXA | −2.1 (−2.9%) | −1.7 | −0.5 | NR | NR | 81 | 23.8  
Fadini et al. (2017) [46] (Italy) | Two-arm RCT | 16 | T2DM | 10 mg | 12 wk | BIA | −3.1 (baseline NR) | −0.1 | −2.9a | NR | −2.4 | 3.2 | 93.5  
Shimizu et al. (2019) [47] (Japan) | Two-arm RCT | 33 | T2DM and NAFLD | 5 mg | 24 wk | BIA | −2.9 (−3.9%) | NR | NR | −0.9 | −1.1 | - | -  
Sugiyama et al. (2018) [48] (Japan) | Two-arm non-randomised cohort study | 28 | T2DM | 5 mg | 6 mo | BIA | −3.4 (−4.4%) | −3.1 | −0.5 | −0.2 | NR | 91.2 | 14.7  
Tobita et al. (2017) [49] (Japan) | Case series (prospective) | 11 | T2DM and NASH | 5 mg | 24 wk | BIA | −3.8 (−4.8%) | −6.1 | 1.2 | 0.1 | 1.2 | 160.5 | LBM increased Fluctuation over 24 wk  
Empagliflozin |  |  |  |  |  |  |  |  |  |  |  |  |   
Ridderstrale et al. (2014) [50] (Global; multi-site) | Two-arm RCT (sub-study) | 46 | T2DM | 25 mg | 104 wk | DXA | NR (−2.8 kg [–3.4%] in entire EMPA group; _n_ =765) | −1.6 | −0.9a | NR | NR | 57.1 | 32.1c  
Javed et al. (2019) [51] (UK) | Two-arm RCT | 19 | PCOS | 25 mg | 12 wk | BIA | −0.8 (−0.8%) | −0.3 | −1.1a | NR | −0.8 | 37.5 | 137.5  
Ipragliflozin |  |  |  |  |  |  |  |  |  |  |  |  |   
Inoue et al. (2019) [52] (Japan) | Two-arm RCT | 24 | T2DM (on insulin therapy) | 50 mg (insulin dose reduced by 20%) | 24 wk | DXA | −2.8 (−4.4%) | −2.1 | −0.6 | NR | NR | 75 | 21.6  
Ohta et al. (2017) [53] (Japan) | Case series (prospective) | 20 | T2DM | 50 mg | 24 wk | DXA | −3.5 (−4.3%) | −1.8 | −1.7 | NR | NR | 51.4 | 48.6  
Iemitsu et al. (2019) [54] (Japan) | Case series (prospective) | 217 | T2DM | 50 mg | 104 wk | BIA | −2.9 (−3.7%) | −1.9 | −1.0a | −0.9 | −0.7 | 31 | 34.0  
Kato et al. (2017) [55] (Japan) | Case series (prospective) | 20 | T2DM | 50 mg | 12 wk | BIA | −1.9 (−2.3%) | −1.3 | −0.6a | −0.6 | −0.7 | 68.4 | 31.6  
Miyake et al. (2018) [56] (Japan) | Case series (prospective) | 12 | T2DM+NAFLD | 50 mg | 24 wk | BIA | −1.4 (−2.1%) | −1.6 | +0.2a | −0.5 | NR | 114.3 | FFM increased  
Osonoi et al. (2018) [57] (Japan) | Case series (prospective) | 98 | T2DM | 50 mg | 24 wk | BIA | −2.1 (−3.2%) | −1.5 | −0.6a | NR | NR | 71.4 | 28.6  
Yamamoto et al. (2016) [58] (Japan) | Case series (prospective) | 24 | T2DM | 50 mg | 16 wk | BIA | −2.5 (−3.3%) | −1.7 | −0.6 | NR | −0.6 | 68 | 24.0  
Luseogliflozin |  |  |  |  |  |  |  |  |  |  |  |  |   
Bouchi et al. (2017) [59] (Japan) | Case series (prospective) | 19 | T2DM | 2.5 mg, titrated up to 5 mg where tolerated and safe | 12 wk | DXA | −2.7 (−3.3%) | −1.4 | −1.1a | NR | NR | 51.9 | 40.7  
Sasaki et al. (2019) [60] (Japan) | Case series (prospective) | 37 | T2DM | 2.5 mg, titrated up to 5 mg where tolerated and safe | 52 wk | DXA | −3.1 (−3.9%) | −2.0 | −1.0 | NR | NR | 64.5 | 32.3  
Tofogliflozin |  |  |  |  |  |  |  |  |  |  |  |  |   
Iwahashi et al. (2016) [61] (Japan) | Case series (prospective) | 17 | T2DM | 20 mg | 48 wk | BIA | −2.3 (−3.0%) | −2.6 | +0.2a | NR | −0.3 | 113 | FFM increased  
Kamei et al. (2018) [62] (Japan) | Case series (retrospective) | 37 | T2DM | 20 mg | 12 wk | BIA | −3.3 (−3.7%) | −1.9 | −1.0 | −0.8 | −0.9 | 57.6 | 30.3  
Matsuba et al. (2018) [63] (Japan) | Case series (prospective) | 14 | T2DM | 20 mg | 12 wk | BIA | −2.9 (baseline NR) | −1.3 | −1.5 | −1.4 | −1.1 | 44.8 | 51.7  
  
[Open in a new tab](table/T3/)

SGLT2i, sodium-glucose cotransporter 2 inhibitor; BW, body weight; FM, fat mass; LBM, lean body mass; DAPA, dapagliflozin; CANA, canagliflozin; T2DM, type 2 diabetes mellitus; NAFLD, nonalcoholic fatty liver disease; BIA, bioelectrical impedance analysis; LUSEO, luseogliflozin; TOFO, tofogliflozin; IPRA, ipragliflozin; NR, not reported; NASH, nonalcoholic steatohepatitis; RCT, randomised controlled trial; DXA, dual-energy X-ray absorptiometry; EMPA, empagliflozin; PCOS, polycystic ovarian syndrome; FFM, fat-free mass.

aFFM; bSame as “Study 1” in Blonde et al. (2016) [41]; cCalculated using mean weight loss in entire cohort.

### Table 4. Studies Reporting Changes in Body Composition with GLP-1RA and SGLT2i Combination Therapy.

Drug/author (location) | Study design | No. (in group prescribed SGLT2i+ GLP-1RA therapy) | Population | Dose (all once-daily, unless otherwise specified; SGLT2i taken as oral tablet, GLP-1RA as subcutaneous injection) | Duration, wk | Tech. used | Total BW change, kg (% change from baseline) | FM change | LBM change | Skeletal muscle mass change, kg | Total body water change, kg | Proportion of weight loss from FM, % | Proportion of weight loss from LBM, %  
---|---|---|---|---|---|---|---|---|---|---|---|---|---  
Dapagliflozin+Exenatide |  |  |  |  |  |  |  |  |  |  |  |  |   
Lundkvist et al. (2017) [67] (Sweden) | Two-arm RCT | 16 | Obesity and pre-diabetes mellitus | DAPA: 10 mg | 52 | MRI | −5.7 (−5.4%) | −5.3 La | −1.4 L | NR | NR | - | -  
EXEN: 2 mg weekly  
Luseogliflozin+Liraglutide |  |  |  |  |  |  |  |  |  |  |  |  |   
Seino et al. (2018) [68] (Japan) | Case series (prospective) (sub-study) | 22 | T2DM | LUSEO: 2.5 mg, titrated up to 5 mg where tolerated and safe (_n_ =17 reached 5 mg dose) | 52 | BIA | −2.9 (−4.3%) | −2.5 kg | −0.4 kg | NR | NR | 86.2 | 13.8  
LIRA: 0.6 mg (_n_ =1) or 0.9 mg (_n_ =21)  
  
[Open in a new tab](table/T4/)

GLP-1RA, glucagon-like peptide-1 receptor agonist; SGLT2i, sodium-glucose cotransporter 2 inhibitor; BW, body weight; FM, fat mass; LBM, lean body mass; RCT, randomised controlled trial; DAPA, dapagliflozin; EXEN, exenatide; MRI, magnetic resonance imaging; NR, not reported; T2DM, type 2 diabetes mellitus; LUSEO, luseogliflozin; LIRA, liraglutide; BIA, bioelectrical impedance analysis.

aTotal adipose tissue.

### Fig. 1. Relative proportions of fat mass and lean body/fat-free mass within total weight loss elicited by glucagon-like peptide-1 receptor agonist therapy [25,26,28,29,31,32,33,35,36,38].

[![Fig. 1](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f399/6769337/7d0827d4eec7/enm-34-247-g001.jpg)](https://www.ncbi.nlm.nih.gov/core/lw/2.0/html/tileshop_pmc/tileshop_pmc_inline.html?title=Click%20on%20image%20to%20zoom&p=PMC3&id=6769337_enm-34-247-g001.jpg)

[Open in a new tab](figure/F1/)

### Fig. 2. Relative proportions of fat mass and lean body/fat-free mass within total weight loss elicited by sodium-glucose cotransporter 2 inhibitor therapy [40,41,42,43,44,45,46,48,50,51,52,53,54,55,57,58,59,60,62,63].

[![Fig. 2](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f399/6769337/2f55c5cd87bb/enm-34-247-g002.jpg)](https://www.ncbi.nlm.nih.gov/core/lw/2.0/html/tileshop_pmc/tileshop_pmc_inline.html?title=Click%20on%20image%20to%20zoom&p=PMC3&id=6769337_enm-34-247-g002.jpg)

[Open in a new tab](figure/F2/)

### Glucagon-like peptide-1 receptor agonists

We identified 17 studies (two within a single manuscript) reporting changes in body composition with liraglutide (_n_ =10), exenatide (_n_ =5), semaglutide (_n_ =1), or dulaglutide (_n_ =1). Thirteen of these studies used DXA to measure body composition, two used BIA alone, one used a combination of BIA and CT, and one used ADP.

#### Liraglutide

Four manuscripts, containing data from five studies, reported relative contributions of LBM/FFM to total weight loss with liraglutide therapy, ranging from 30% to 47% [26,28,29,32]. This included data from separate sub-studies contained within two phase 3 trials in the Liraglutide Effect and Action in Diabetes (LEAD) programme. In LEAD 2, there was a step-wise increase in the magnitude of both total weight loss (0.9, 2.0, and 3.2 kg) and the relative contribution of LBM (33%, 40%, and 47%) when liraglutide was prescribed at 0.6, 1.2, and 1.8 mg once-daily for 26 weeks [28]. In LEAD 3, 1.2 mg liraglutide elicited 2.4 kg weight loss after 52 weeks, with 46% comprising LBM. Liraglutide 1.8 mg resulted in a similar magnitude of weight loss at 52 weeks (2.3 kg) but the relative contribution of LBM was considerably higher (65.2%). Two further trials using DXA report similar results in individuals with obesity and T2DM (30% of 5.0 kg weight loss over 12 weeks) [29] and polycystic ovarian syndrome (PCOS; 46% of 5.2 kg weight loss over 26 weeks) [26]. Furthermore, a single study, using BIA, reported 5.6 kg weight loss after 24 weeks of liraglutide therapy in individuals with type 1 diabetes mellitus, of which 41% constituted LBM [32].

Conversely, two studies report a comparatively smaller contribution of LBM/FFM to total weight loss with 24 weeks liraglutide treatment in overweight/obese individuals with T2DM (16% of 2.5 kg weight loss with 3 mg once-daily), and those with T2DM and non-alcoholic fatty liver disease (NAFLD) (4% of 5.6 kg weight loss) [25,31]. Furthermore, two studies report no change or marginal increases in LBM after 24 and 8 weeks of treatment, respectively [27,30]. The final manuscript identified reductions in LBM in individuals undergoing liraglutide therapy at 1.2, 1.8, 2.4, and 3.0 mg for 20 weeks, but did not report total BW change for the subset of individuals undergoing body composition assessment [24].

#### Exenatide

We identified five studies examining the impact of exenatide on body composition, reporting similar, although more heterogeneous, findings than those for liraglutide. Two studies used DXA to assess changes in body composition with 14 weeks of exenatide treatment in individuals with obesity with or without schizophrenia. These studies reported a mean weight loss of 2.0 and 2.3 kg, respectively, of which the relative contribution of FFM to total weight loss was 40% and 52% [33,35].

A separate study reported 3.5 kg weight loss after 16 weeks of treatment in individuals with overweight/obesity and T2DM and reported that 11.4% of weight loss comprised of LBM [36]. One further study reported a small increase (0.3 kg) in LBM after 52 weeks of treatment in individuals with T2DM [34]. The final study identified (utilising BIA to assess changes in body composition over 12 weeks in individuals with obesity and T2DM), did not report changes in LBM, but reported a loss of FM that was greater than the total weight loss induced by exenatide, and a 1.3 kg increase in skeletal muscle [37].

#### Semaglutide

The only study administering semaglutide reported 5.0 kg weight loss after 12 weeks of treatment in individuals with obesity [38]. Of this weight loss, 20% was estimated, using ADP, to consist of LBM.

#### Dulaglutide

A single study of dulaglutide reported data from a case series of five individuals assessed before and after 12 weeks of treatment, using BIA [39]. This manuscript did not report total weight loss, but outlined 0.1 and 0.2 kg loss of LBM and skeletal muscle, respectively, in comparison to 1.9 kg loss of FM.

### Sodium-glucose cotransporter 2 inhibitors

We identified 27 studies examining changes in body composition with SGLT2i therapy (canagliflozin=4, dapagliflozin=6, empagliflozin=2, ipragliflozin=7, luseogliflozin=2, tofogliflozin=3, and various=3). Compared to the studies identified for GLP-1RAs, there was much greater use of BIA (17 studies), with the remaining 10 using DXA.

#### Canagliflozin

The most robust evidence of body composition changes with SGLT2i comes from a pair of randomised controlled trials (RCTs) reporting data on the effects of canagliflozin on DXA-derived body composition in individuals with T2DM [40,41]. After 26 weeks, canagliflozin resulted in 2.5 and 3.2 kg weight loss at 100 and 300 mg, respectively, with the relative contribution of weight loss attributed to LBM being 36.0% and 37.5% [40]. At 52 weeks, the magnitude of weight loss was numerically greater than at 26 weeks and similar with both doses (−4.4 and −4.2 kg), but the relative contribution attributed to LBM was lower (20.5% and 26.2%, respectively) [41]. A third study using DXA, resulted in 2.4 kg weight loss in individuals with T2DM, of which almost 46% was LBM [42].

A single study, using BIA to assess body composition changes over a longer period of time (1 year), reported the contribution of LBM to a total 2.9 kg weight loss in individuals with T2DM and NAFLD to be lower than the studies above, at just 6.9% [43].

#### Dapagliflozin

Two studies report changes in body composition with dapagliflozin therapy using DXA, providing similar findings to those for canagliflozin. In a sub-study contained within a large multi-site RCT, total weight loss after 104 weeks of dapagliflozin therapy in 69 participants with overweight/obesity and T2DM was 4.5 kg, of which 28.9% consisted of LBM [45]. A smaller, shorter study reported a lower magnitude of weight loss over 12 weeks (2.1 kg) but with a similar relative contribution of LBM (23.8%) [45].

Three further studies, using BIA, show heterogeneous findings. In a non-randomised trial in which 50 individuals with T2DM were prescribed dapagliflozin or non-SGLT2i therapies for 6 months, the dapagliflozin group lost a mean 3.4 kg of BW, of which 15% constituted LBM [48]. In contrast, a 12-week RCT reported that almost all (94%) of the 3.1 kg weight loss elicited with dapagliflozin was LBM [46], whilst a small cohort study in 11 individuals with T2DM and nonalcoholic steatohepatitis, reported a 1.2 kg increase in LBM after 24 weeks of treatment, despite 3.8 kg weight loss [49].

#### Empagliflozin

Only two studies report the impact of empagliflozin on body composition. One of these provides DXA-derived data from a sub-study nested within the global EMPA-REG H2H-SU trial; a randomised head-to-head trial conducted as part of the empagliflozin phase 3 programme, which compared empagliflozin with the sulphonylurea glimepiride as second line therapy alongside metformin in individuals with T2DM [50]. Using the weight loss observed in the entire population after 104 weeks (2.8 kg), it was estimated that approximately one-third (32.1%) of weight loss elicited in this trial comprised of FFM.

A separate study using BIA to assess changes with empagliflozin in women with PCOS reported a loss of FFM (1.1 kg) which was greater than the total weight loss elicited (0.8 kg) [51].

#### Ipragliflozin

Of the seven studies identified for ipragliflozin, six reported that the contribution of LBM/FFM to total weight loss elicited ranged from 22% to 49% [52,53,54,55,57,59]. All of these studies were in individuals with T2DM, and included two studies using DXA, reporting 22% and 49% of 2.8 and 3.5 kg of weight loss over 24 weeks, respectively. The remaining four used BIA and in the largest and longest of these studies (_n_ =217; 104 weeks of treatment), the proportion of weight loss attributed to FFM was approximately one-third (34.5%) [54].

In the final study identified, 24 weeks of treatment with ipragliflozin resulted in a loss of FM that was greater than the total weight loss in individuals with T2DM and NAFLD, inferring a small gain (0.2 kg) in FFM [56].

#### Luseogliflozin

Two studies examining the effects of luseogliflozin on body composition using DXA report similar findings to those outlined above. Both studies were in individuals with T2DM, reporting that 12 and 52 weeks of luseogliflozin therapy elicited 2.7 and 3.1 kg weight loss, respectively. Of this weight loss, 41% and 32% could be attributed to a loss of FFM/LBM [59,60].

#### Tofogliflozin

Three similar studies, each utilising BIA, report data regarding the impact of tofogliflozin in individuals with T2DM. Two assessed changes over 12 weeks, reporting 3.3 and 2.9 kg weight loss, of which 30% and 52%, respectively, could be attributed to LBM [62,63]. The third study reported 2.3 kg weight loss in 17 individuals with T2DM despite an estimated 0.2 kg increase in FFM [61].

#### Various

Three cohort studies (two retrospective, one prospective) report data from participants prescribed SGLT2i therapy, with variation in the exact agent prescribed [64,65,66]. Each utilised BIA to assess body composition, but only one study reported changes in both total BW and FFM. In this study, almost half (48%) of the 5.2 kg weight loss elicited by dapagliflozin (_n_ =10) or canagliflozin (_n_ =7), in individuals with overweight/obesity, T2DM and NAFLD, could be attributed to FFM [64].

### Dual therapy of GLP-1RAs and SGLT2is in combination

We identified two studies reporting the effect of GLP-1RAs and SGLT2is in combination. In the first, individuals with obesity and pre-diabetes mellitus were randomised to dapagliflozin-plus-exenatide or placebo for 24 weeks, with an open-label extension (during which all participants underwent combined therapy) for a further 28 weeks [67]. Weight loss after 52 weeks was 5.7 kg, with MRI demonstrating reductions in both adipose tissue and lean tissue volumes (5.3 and 1.4 L, respectively). The second study, using BIA, reported 2.9 kg weight loss after 52 weeks of treatment with luseogliflozin-plus-liraglutide, in individuals with T2DM, of which 14% comprised of LBM [68].

## CONCLUSIONS

Our review demonstrates that, in most circumstances, the weight loss associated with GLP-1RA and/or SGLT2i therapy comprises predominantly of a reduction in FM. Within 53 groups of individuals prescribed GLP-1RA, SGLT2i or combined therapy, only five groups saw a loss of LBM, FFM, or skeletal muscle mass (depending on reporting) that was greater than FM lost. Thus, in most cases, body composition (i.e., the ratio of FM to FFM/LBM) was more favorable after treatment than before.

However, out of 43 groups in which it was possible to estimate the proportion of weight loss that could be attributed to LBM/FFM, in 27 groups this proportion of LBM lost ranged from 20% to 50%. Six groups saw a loss of LBM, FFM, or skeletal muscle but with a smaller relative contribution to total weight loss (0% to 19%), whilst another six reported an increase in LBM, FFM, or skeletal muscle mass. These results are in accordance with studies of diet-induced weight loss and bariatric surgery, which have reported similar proportions of LBM/FFM (approximately 25% to 40%) within total weight loss elicited [8,9,69,70,71].

There were no clear differences between GLP-1RAs and SGLT2is in the magnitude of weight loss that could be attributed to LBM/FFM. However, it should be noted that the number of studies identified for each class of therapy was relatively small, and these were divided further between multiple therapies within each class. There was also heterogeneity of findings within each drug class and within individual therapies. Reasons for this heterogeneity may include (1) the specific therapy used, (2) the dose or duration of treatment, (3) concomitant therapies alongside GLP-1RA or SGLT2i treatment (e.g., if examined as monotherapy or dual-therapy with other glucose-lowering therapies such as metformin), (4) the clinical diagnoses, background medication usage (i.e., use of sulphonylureas or insulin) and other baseline characteristics of the study population, (5) the technique used to assess body composition (including standardisation procedures prior to measurement), and (6) natural inter-individual variation in responses to weight loss. There are no clear patterns within the data collected in this review, including treatment type, dose or duration and the technique used.

Although the benefits of weight loss in obesity-associated chronic metabolic disease are well established [72,73], these favorable outcomes may be limited by losses in LBM, particularly if this constitutes skeletal muscle. Of the studies identified in this review, 12 reported changes in either LBM or FFM, along with changes in skeletal muscle. In seven of these studies, the loss of skeletal muscle comprised between 55% and 100% of the LBM/FFM lost. In one it was twice as great [40], whilst another reported a loss of skeletal muscle despite an increase in FFM [56]. The mechanism of action of SGLT2i, inducing polyuria alongside glycosuria, means that SGLT2i therapy is also associated with fluid loss [1]. Reductions in TBW may impact on body composition measurement, and it could be speculated that reductions in FFM/LBM with weight loss may reflect reductions in water content. Of the 27 studies identified reporting changes in body composition with SGLT2i therapy, 13 reported changes in TBW, 12 of which reported losses; ranging from 0.2 to 2.4 kg (7% to 100% of total weight loss). It is imperative to stress; however, that water is contained within both adipose- and non-adipose tissues [74], and all of the studies reporting TBW in this review utilised BIA. TBW, as provided by BIA, does not allow distinction between water contained in, and thus lost from, different body compartments (i.e., FM or FFM/LBM). No studies reported change in TBW with GLP-1RA or combination therapy.

Consequently, although yielding a more favorable body composition, the potential LBM and skeletal muscle loss associated with weight loss induced by GLP-1RAs and SGLT2is warrants attention. A more rapid decline in skeletal muscle and consequential increased risk of sarcopenia is concerning, particularly as individuals prescribed these therapies are usually already vulnerable to an increased risk of physical frailty (i.e., those with T2DM and/or obesity) [75]. In turn, strategies to preserve or increase skeletal muscle and physical function in these individuals (e.g., through structured exercise training), are of importance. It is also important to state that the absolute mass of skeletal muscle is not the only factor to consider, and improving muscular function (strength, endurance, flexibility etc.) remains critical to improve physical function and performance in tasks of daily living; to impact positively on an individual's quality of life.

Previous studies have shown that markers of physical function (including balance, grip strength, and gait speed) improve following diet-induced weight loss [9,76,77], likely by reducing the biomechanical burden of moving around [78]. However, when diet and exercise are combined, improvements in physical function are greater than that elicited by diet alone, which may be underpinned by preserved or improved skeletal muscle mass and function [9,79]. Whether the same results are observed when exercise is combined with weight loss elicited by GLP-1RAs and SGLT2i therapy is currently unknown, and requires testing in robust experimental research.

Collectively, the available evidence suggests that the initiation of GLP-1RA or SGLT2i therapy results in weight loss that is primarily derived of FM. However, this is accompanied by reductions in LBM which are not insignificant and should be considered in parallel to changes in FM and overall BW. Differences between therapies are currently unclear. A substantial proportion of LBM loss may be comprised of skeletal muscle, which may be clinically relevant, particularly in the populations to which these therapies are prescribed. Given the heterogeneity, a more consistent approach to measurement and reporting of body composition in future research would be beneficial. As the prevalence of obesity, physical inactivity and associated co-morbidities, including T2DM, continues to rise, it is imperative to explore strategies to preserve LBM and improve physical function, and particularly their interaction with glucose-lowering strategies that positively impact on weight loss.

## ACKNOWLEDGMENTS

This research was supported by the National Institute for Health Research (NIHR) Leicester Biomedical Research Centre (BRC) and the NIHR Collaboration for Leadership in Applied Health Research and Care–East Midlands (CLAHRC–EM). The views expressed are those of the authors and not necessarily those of the National Health Service, the National Institute for Health Research, or the Department of Health.

## Footnotes

**CONFLICTS OF INTEREST:** Melanie Jane Davies has acted as a consultant, advisory board member and speaker for Novo Nordisk, Sanofi-Aventis, Lilly, Merck Sharp & Dohme, Boehringer Ingelheim, AstraZeneca and Janssen, an advisory board member for Servier and as a speaker for Mitsubishi Tanabe Pharma Corporation and Takeda Pharmaceuticals International Inc. She has received grants in support of investigator and investigator-initiated trials from Novo Nordisk, Sanofi-Aventis, Lilly, Boehringer Ingelheim, and Janssen.

Kamlesh Khunti has received honoraria from Abbot, AstraZeneca, Berlin-Chemie AG/Menarini Group, Boehringer Ingelheim, Eli Lilly, Janssen, Merck Sharp & Dohme, Novartis, Novo Nordisk, Roche, and Sanofi, and research support from AstraZeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck Sharp & Dohme, Novartis, Novo Nordisk, Roche, and Sanofi.

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