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Abstract page for arXiv paper 2412.05127v1: The Prompt Canvas: A Literature-Based Practitioner Guide for Creating Effective Prompts in Large Language Models


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The Prompt Canvas: A Literature-Based Practitioner Guide for Creating Effective Prompts in Large Language Models


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The rise of large language models (LLMs) has highlighted the importance of prompt engineering as a crucial technique for optimizing model outputs. While experimentation with various prompting methods, such as Few-shot, Chain-of-Thought, and role-based techniques, has yielded promising results, these advancements remain fragmented across academic papers, blog posts and anecdotal experimentation. The lack of a single, unified resource to consolidate the field's knowledge impedes the progress of both research and practical application. This paper argues for the creation of an overarching framework that synthesizes existing methodologies into a cohesive overview for practitioners. Using a design-based research approach, we present the Prompt Canvas, a structured framework resulting from an extensive literature review on prompt engineering that captures current knowledge and expertise. By combining the conceptual foundations and practical strategies identified in prompt engineering, the Prompt Canvas provides a practical approach for leveraging the potential of Large Language Models. It is primarily designed as a learning resource for pupils, students and employees, offering a structured introduction to prompt engineering. This work aims to contribute to the growing discourse on prompt engineering by establishing a unified methodology for researchers and providing guidance for practitioners.


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Abstract page for arXiv paper 2410.19385v1: Investigating the Role of Prompting and External Tools in Hallucination Rates of Large Language Models


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Investigating the Role of Prompting and External Tools in Hallucination Rates of Large Language Models


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Large Language Models (LLMs) are powerful computational models trained on extensive corpora of human-readable text, enabling them to perform general-purpose language understanding and generation. LLMs have garnered significant attention in both industry and academia due to their exceptional performance across various natural language processing (NLP) tasks. Despite these successes, LLMs often produce inaccuracies, commonly referred to as hallucinations. Prompt engineering, the process of designing and formulating instructions for LLMs to perform specific tasks, has emerged as a key approach to mitigating hallucinations. This paper provides a comprehensive empirical evaluation of different prompting strategies and frameworks aimed at reducing hallucinations in LLMs. Various prompting techniques are applied to a broad set of benchmark datasets to assess the accuracy and hallucination rate of each method. Additionally, the paper investigates the influence of tool-calling agents (LLMs augmented with external tools to enhance their capabilities beyond language generation) on hallucination rates in the same benchmarks. The findings demonstrate that the optimal prompting technique depends on the type of problem, and that simpler techniques often outperform more complex methods in reducing hallucinations. Furthermore, it is shown that LLM agents can exhibit significantly higher hallucination rates due to the added complexity of external tool usage.


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Abstract page for arXiv paper 2410.11123v1: A Systematic Review on Prompt Engineering in Large Language Models for K-12 STEM Education


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Large language models (LLMs) have the potential to enhance K-12 STEM education by improving both teaching and learning processes. While previous studies have shown promising results, there is still a lack of comprehensive understanding regarding how LLMs are effectively applied, specifically through prompt engineering-the process of designing prompts to generate desired outputs. To address this gap, our study investigates empirical research published between 2021 and 2024 that explores the use of LLMs combined with prompt engineering in K-12 STEM education. Following the PRISMA protocol, we screened 2,654 papers and selected 30 studies for analysis. Our review identifies the prompting strategies employed, the types of LLMs used, methods of evaluating effectiveness, and limitations in prior work. Results indicate that while simple and zero-shot prompting are commonly used, more advanced techniques like few-shot and chain-of-thought prompting have demonstrated positive outcomes for various educational tasks. GPT-series models are predominantly used, but smaller and fine-tuned models (e.g., Blender 7B) paired with effective prompt engineering outperform prompting larger models (e.g., GPT-3) in specific contexts. Evaluation methods vary significantly, with limited empirical validation in real-world settings.


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Abstract page for arXiv paper 2401.01313v3: A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models


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As Large Language Models (LLMs) continue to advance in their ability to write human-like text, a key challenge remains around their tendency to hallucinate generating content that appears factual but is ungrounded. This issue of hallucination is arguably the biggest hindrance to safely deploying these powerful LLMs into real-world production systems that impact people's lives. The journey toward widespread adoption of LLMs in practical settings heavily relies on addressing and mitigating hallucinations. Unlike traditional AI systems focused on limited tasks, LLMs have been exposed to vast amounts of online text data during training. While this allows them to display impressive language fluency, it also means they are capable of extrapolating information from the biases in training data, misinterpreting ambiguous prompts, or modifying the information to align superficially with the input. This becomes hugely alarming when we rely on language generation capabilities for sensitive applications, such as summarizing medical records, financial analysis reports, etc. This paper presents a comprehensive survey of over 32 techniques developed to mitigate hallucination in LLMs. Notable among these are Retrieval Augmented Generation (Lewis et al, 2021), Knowledge Retrieval (Varshney et al,2023), CoNLI (Lei et al, 2023), and CoVe (Dhuliawala et al, 2023). Furthermore, we introduce a detailed taxonomy categorizing these methods based on various parameters, such as dataset utilization, common tasks, feedback mechanisms, and retriever types. This classification helps distinguish the diverse approaches specifically designed to tackle hallucination issues in LLMs. Additionally, we analyze the challenges and limitations inherent in these techniques, providing a solid foundation for future research in addressing hallucinations and related phenomena within the realm of LLMs.


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Abstract page for arXiv paper 2502.06039v1: Benchmarking Prompt Engineering Techniques for Secure Code Generation with GPT Models


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Prompt engineering reduces reasoning mistakes in Large Language Models (LLMs). However, its effectiveness in mitigating vulnerabilities in LLM-generated code remains underexplored. To address this gap, we implemented a benchmark to automatically assess the impact of various prompt engineering strategies on code security. Our benchmark leverages two peer-reviewed prompt datasets and employs static scanners to evaluate code security at scale. We tested multiple prompt engineering techniques on GPT-3.5-turbo, GPT-4o, and GPT-4o-mini. Our results show that for GPT-4o and GPT-4o-mini, a security-focused prompt prefix can reduce the occurrence of security vulnerabilities by up to 56%. Additionally, all tested models demonstrated the ability to detect and repair between 41.9% and 68.7% of vulnerabilities in previously generated code when using iterative prompting techniques. Finally, we introduce a "prompt agent" that demonstrates how the most effective techniques can be applied in real-world development workflows.


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Abstract page for arXiv paper 2501.01329v1: The Prompt Alchemist: Automated LLM-Tailored Prompt Optimization for Test Case Generation


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Test cases are essential for validating the reliability and quality of software applications. Recent studies have demonstrated the capability of Large Language Models (LLMs) to generate useful test cases for given source code. However, the existing work primarily relies on human-written plain prompts, which often leads to suboptimal results since the performance of LLMs can be highly influenced by the prompts. Moreover, these approaches use the same prompt for all LLMs, overlooking the fact that different LLMs might be best suited to different prompts. Given the wide variety of possible prompt formulations, automatically discovering the optimal prompt for each LLM presents a significant challenge. Although there are methods on automated prompt optimization in the natural language processing field, they are hard to produce effective prompts for the test case generation task. First, the methods iteratively optimize prompts by simply combining and mutating existing ones without proper guidance, resulting in prompts that lack diversity and tend to repeat the same errors in the generated test cases. Second, the prompts are generally lack of domain contextual knowledge, limiting LLMs' performance in the task.


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Abstract page for arXiv paper 2311.03754v1: Which is better? Exploring Prompting Strategy For LLM-based Metrics


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This paper describes the DSBA submissions to the Prompting Large Language Models as Explainable Metrics shared task, where systems were submitted to two tracks: small and large summarization tracks. With advanced Large Language Models (LLMs) such as GPT-4, evaluating the quality of Natural Language Generation (NLG) has become increasingly paramount. Traditional similarity-based metrics such as BLEU and ROUGE have shown to misalign with human evaluation and are ill-suited for open-ended generation tasks. To address this issue, we explore the potential capability of LLM-based metrics, especially leveraging open-source LLMs. In this study, wide range of prompts and prompting techniques are systematically analyzed with three approaches: prompting strategy, score aggregation, and explainability. Our research focuses on formulating effective prompt templates, determining the granularity of NLG quality scores and assessing the impact of in-context examples on LLM-based evaluation. Furthermore, three aggregation strategies are compared to identify the most reliable method for aggregating NLG quality scores. To examine explainability, we devise a strategy that generates rationales for the scores and analyzes the characteristics of the explanation produced by the open-source LLMs. Extensive experiments provide insights regarding evaluation capabilities of open-source LLMs and suggest effective prompting strategies.


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Abstract page for arXiv paper 2404.06404v1: Apprentices to Research Assistants: Advancing Research with Large Language Models


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Large Language Models (LLMs) have emerged as powerful tools in various research domains. This article examines their potential through a literature review and firsthand experimentation. While LLMs offer benefits like cost-effectiveness and efficiency, challenges such as prompt tuning, biases, and subjectivity must be addressed. The study presents insights from experiments utilizing LLMs for qualitative analysis, highlighting successes and limitations. Additionally, it discusses strategies for mitigating challenges, such as prompt optimization techniques and leveraging human expertise. This study aligns with the 'LLMs as Research Tools' workshop's focus on integrating LLMs into HCI data work critically and ethically. By addressing both opportunities and challenges, our work contributes to the ongoing dialogue on their responsible application in research.


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Abstract page for arXiv paper 2310.14735v5: Unleashing the potential of prompt engineering in Large Language Models: a comprehensive review


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This comprehensive review delves into the pivotal role of prompt engineering in unleashing the capabilities of Large Language Models (LLMs). The development of Artificial Intelligence (AI), from its inception in the 1950s to the emergence of advanced neural networks and deep learning architectures, has made a breakthrough in LLMs, with models such as GPT-4o and Claude-3, and in Vision-Language Models (VLMs), with models such as CLIP and ALIGN. Prompt engineering is the process of structuring inputs, which has emerged as a crucial technique to maximize the utility and accuracy of these models. This paper explores both foundational and advanced methodologies of prompt engineering, including techniques such as self-consistency, chain-of-thought, and generated knowledge, which significantly enhance model performance. Additionally, it examines the prompt method of VLMs through innovative approaches such as Context Optimization (CoOp), Conditional Context Optimization (CoCoOp), and Multimodal Prompt Learning (MaPLe). Critical to this discussion is the aspect of AI security, particularly adversarial attacks that exploit vulnerabilities in prompt engineering. Strategies to mitigate these risks and enhance model robustness are thoroughly reviewed. The evaluation of prompt methods is also addressed, through both subjective and objective metrics, ensuring a robust analysis of their efficacy. This review also reflects the essential role of prompt engineering in advancing AI capabilities, providing a structured framework for future research and application.


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Abstract page for arXiv paper 2408.13184v2: Can LLM be a Good Path Planner based on Prompt Engineering? Mitigating the Hallucination for Path Planning


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Can LLM be a Good Path Planner based on Prompt Engineering? Mitigating the Hallucination for Path Planning


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Spatial reasoning in Large Language Models (LLMs) is the foundation for embodied intelligence. However, even in simple maze environments, LLMs still encounter challenges in long-term path-planning, primarily influenced by their spatial hallucination and context inconsistency hallucination by long-term reasoning. To address this challenge, this study proposes an innovative model, Spatial-to-Relational Transformation and Curriculum Q-Learning (S2RCQL). To address the spatial hallucination of LLMs, we propose the Spatial-to-Relational approach, which transforms spatial prompts into entity relations and paths representing entity relation chains. This approach fully taps the potential of LLMs in terms of sequential thinking. As a result, we design a path-planning algorithm based on Q-learning to mitigate the context inconsistency hallucination, which enhances the reasoning ability of LLMs. Using the Q-value of state-action as auxiliary information for prompts, we correct the hallucinations of LLMs, thereby guiding LLMs to learn the optimal path. Finally, we propose a reverse curriculum learning technique based on LLMs to further mitigate the context inconsistency hallucination. LLMs can rapidly accumulate successful experiences by reducing task difficulty and leveraging them to tackle more complex tasks. We performed comprehensive experiments based on Baidu's self-developed LLM: ERNIE-Bot 4.0. The results showed that our S2RCQL achieved a 23%--40% improvement in both success and optimality rates compared with advanced prompt engineering.


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Abstract page for arXiv paper 2410.20024v1: Beyond Fine-Tuning: Effective Strategies for Mitigating Hallucinations in Large Language Models for Data Analytics


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Beyond Fine-Tuning: Effective Strategies for Mitigating Hallucinations in Large Language Models for Data Analytics


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Large Language Models (LLMs) have become increasingly important in natural language processing, enabling advanced data analytics through natural language queries. However, these models often generate "hallucinations"-inaccurate or fabricated information-that can undermine their reliability in critical data-driven decision-making. Addressing the challenge of hallucinations is essential to improve the accuracy and trustworthiness of LLMs in processing natural language queries. This research focuses on mitigating hallucinations in LLMs, specifically within the context of data analytics. We introduce and evaluate four targeted strategies: Structured Output Generation, Strict Rules Enforcement, System Prompt Enhancements, and Semantic Layer Integration. Our findings show that these methods are more effective than traditional fine-tuning approaches in reducing hallucinations, offering a more reliable framework for deploying LLMs in natural language queries for data analytics. This research demonstrates the potential of these strategies to enhance the accuracy of LLM-driven data queries, ensuring more dependable results in data-driven environments.


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Abstract page for arXiv paper 2306.03799v2: Prompt Space Optimizing Few-shot Reasoning Success with Large Language Models


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Prompt engineering is an essential technique for enhancing the abilities of large language models (LLMs) by providing explicit and specific instructions. It enables LLMs to excel in various tasks, such as arithmetic reasoning, question answering, summarization, relation extraction, machine translation, and sentiment analysis. Researchers have been actively exploring different prompt engineering strategies, such as Chain of Thought (CoT), Zero-CoT, and In-context learning. However, an unresolved problem arises from the fact that current approaches lack a solid mathematical solution for determining optimal prompts. To address this issue in prompt engineering, we propose a new and effective approach called Prompt Space. Our methodology utilizes text embeddings to obtain basis vectors by matrix decomposition, and then constructs a space for representing all prompts. Prompt Space significantly outperforms state-of-the-art prompt paradigms on ten public reasoning benchmarks. Notably, without the help of the CoT method and the prompt "Let's think step by step", Prompt Space shows superior performance over the few-shot method. Overall, our approach provides a robust and effective mathematical framework for selecting simple and effective prompts. This advancement marks a significant step towards improving prompt engineering for a wide variety of applications in LLMs. Our code is publicly available at \textcolor{blue}{\url{https://github.com/YouBLEI/Prompt-Space}}


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Prompting is a crucial aspect of working with large language models (LLMs). It involves providing clear and specific instructions to guide the model’s output. 
Let’s explore the principles and…


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Prompting is a crucial aspect of working with large language models (LLMs). It involves providing clear and specific instructions to guide…


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Abstract page for arXiv paper 2310.01441v2: UPAR: A Kantian-Inspired Prompting Framework for Enhancing Large Language Model Capabilities


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Large Language Models (LLMs) have demonstrated impressive inferential capabilities, with numerous research endeavors devoted to enhancing this capacity through prompting. Despite these efforts, a unified epistemological foundation is still conspicuously absent. Drawing inspiration from Kant's a priori philosophy, we propose the UPAR prompting framework, designed to emulate the structure of human cognition within LLMs. The UPAR framework is delineated into four phases: "Understand", "Plan", "Act", and "Reflect", enabling the extraction of structured information from complex contexts, prior planning of solutions, execution according to plan, and self-reflection. This structure significantly augments the explainability and accuracy of LLM inference, producing a human-understandable and inspectable inferential trajectory. Furthermore, our work offers an epistemological foundation for existing prompting techniques, allowing for a possible systematic integration of these methods. With GPT-4, our approach elevates the accuracy from COT baseline of 22.92% to 58.33% in a challenging subset of GSM8K, and from 67.91% to 75.40% in the causal judgment task. Without using few-shot examples or external tools, UPAR significantly outperforms existing prompting methods on SCIBENCH, a challenging dataset containing collegiate-level mathematics, chemistry, and physics scientific problems.


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Abstract page for arXiv paper 2210.02441: Ask Me Anything: A simple strategy for prompting language models


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Large language models (LLMs) transfer well to new tasks out-of-the-box simply given a natural language prompt that demonstrates how to perform the task and no additional training. Prompting is a brittle process wherein small modifications to the prompt can cause large variations in the model predictions, and therefore significant effort is dedicated towards designing a painstakingly "perfect prompt" for a task. To mitigate the high degree of effort involved in prompt-design, we instead ask whether producing multiple effective, yet imperfect, prompts and aggregating them can lead to a high quality prompting strategy. Our observations motivate our proposed prompting method, ASK ME ANYTHING (AMA). We first develop an understanding of the effective prompt formats, finding that question-answering (QA) prompts, which encourage open-ended generation ("Who went to the park?") tend to outperform those that restrict the model outputs ("John went to the park. Output True or False."). Our approach recursively uses the LLM itself to transform task inputs to the effective QA format. We apply the collected prompts to obtain several noisy votes for the input's true label. We find that the prompts can have very different accuracies and complex dependencies and thus propose to use weak supervision, a procedure for combining the noisy predictions, to produce the final predictions for the inputs. We evaluate AMA across open-source model families (e.g., EleutherAI, BLOOM, OPT, and T0) and model sizes (125M-175B parameters), demonstrating an average performance lift of 10.2% over the few-shot baseline. This simple strategy enables the open-source GPT-J-6B model to match and exceed the performance of few-shot GPT3-175B on 15 of 20 popular benchmarks. Averaged across these tasks, the GPT-J-6B model outperforms few-shot GPT3-175B. We release our code here: https://github.com/HazyResearch/ama_prompting


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Autoregressive Large Language Models have transformed the landscape of Natural Language Processing. Pre-train and prompt paradigm has replaced the conventional approach of pre-training and fine-tuning for many downstream NLP tasks. This shift has been possible largely due to LLMs and innovative prompting techniques. LLMs have shown great promise for a variety of downstream tasks owing to their vast parameters and huge datasets that they are pre-trained on. However, in order to fully realize their potential, their outputs must be guided towards the desired outcomes. Prompting, in which a specific input or instruction is provided to guide the LLMs toward the intended output, has become a tool for achieving this goal. In this paper, we discuss the various prompting techniques that have been applied to fully harness the power of LLMs. We present a taxonomy of existing literature on prompting techniques and provide a concise survey based on this taxonomy. Further, we identify some open problems in the realm of prompting in autoregressive LLMs which could serve as a direction for future research.


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Abstract page for arXiv paper 2311.03754: Which is better? Exploring Prompting Strategy For LLM-based Metrics


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Large Language Models (LLMs) are highly sensitive to prompts, including additional context provided therein. As LLMs grow in capability, understanding their prompt-sensitivity becomes increasingly crucial for ensuring reliable and robust performance, particularly since evaluating these models becomes more challenging. In this work, we investigate how current models (Llama, Mixtral, Falcon) respond when presented with additional input from another model, mimicking a scenario where a more capable model -- or a system with access to more external information -- provides supplementary information to the target model. Across a diverse spectrum of question-answering tasks, we study how an LLM's response to multiple-choice questions changes when the prompt includes a prediction and explanation from another model. Specifically, we explore the influence of the presence of an explanation, the stated authoritativeness of the source, and the stated confidence of the supplementary input. Our findings reveal that models are strongly influenced, and when explanations are provided they are swayed irrespective of the quality of the explanation. The models are more likely to be swayed if the input is presented as being authoritative or confident, but the effect is small in size. This study underscores the significant prompt-sensitivity of LLMs and highlights the potential risks of incorporating outputs from external sources without thorough scrutiny and further validation. As LLMs continue to advance, understanding and mitigating such sensitivities will be crucial for their reliable and trustworthy deployment.


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The notion of a prompt is not new. This idea of offering a prompt permeates many disciplines: arts (to spur a writer or to speak spontaneously); science (to commence an experiment); criminal…


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Abstract page for arXiv paper 2410.13073: PromptExp: Multi-granularity Prompt Explanation of Large Language Models


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Large Language Models excel in tasks like natural language understanding and text generation. Prompt engineering plays a critical role in leveraging LLM effectively. However, LLMs black-box nature hinders its interpretability and effective prompting engineering. A wide range of model explanation approaches have been developed for deep learning models, However, these local explanations are designed for single-output tasks like classification and regression,and cannot be directly applied to LLMs, which generate sequences of tokens. Recent efforts in LLM explanation focus on natural language explanations, but they are prone to hallucinations and inaccuracies. To address this, we introduce PromptExp , a framework for multi-granularity prompt explanations by aggregating token-level insights. PromptExp introduces two token-level explanation approaches: 1. an aggregation-based approach combining local explanation techniques, and 2. a perturbation-based approach with novel techniques to evaluate token masking impact. PromptExp supports both white-box and black-box explanations and extends explanations to higher granularity levels, enabling flexible analysis. We evaluate PromptExp in case studies such as sentiment analysis, showing the perturbation-based approach performs best using semantic similarity to assess perturbation impact. Furthermore, we conducted a user study to confirm PromptExp's accuracy and practical value, and demonstrate its potential to enhance LLM interpretability.


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Abstract page for arXiv paper 2311.09782v2: More Samples or More Prompts? Exploring Effective In-Context Sampling for LLM Few-Shot Prompt Engineering


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More Samples or More Prompts? Exploring Effective In-Context Sampling for LLM Few-Shot Prompt Engineering


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While most existing works on LLM prompting techniques focus only on how to select a better set of data samples inside one single prompt input (In-Context Learning or ICL), why can not we design and leverage multiple prompts together to further improve the LLM's performance? In this work, we propose In-Context Sampling (ICS), a low-resource LLM prompting technique to produce confident predictions by optimizing the construction of multiple ICL prompt inputs. Extensive experiments with three open-source LLMs (FlanT5-XL, Mistral-7B, and Mixtral-8x7B) on four NLI datasets (e-SNLI, Multi-NLI, ANLI, and Contract-NLI) and one QA dataset (CommonsenseQA) illustrate that ICS can consistently enhance LLMs' performance. An in-depth evaluation with three data similarity-based ICS strategies suggests that these strategies can further elevate LLM's performance, which sheds light on a new yet promising future research direction.


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Abstract page for arXiv paper 2302.11382v1: A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT


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Prompt engineering is an increasingly important skill set needed to converse effectively with large language models (LLMs), such as ChatGPT. Prompts are instructions given to an LLM to enforce rules, automate processes, and ensure specific qualities (and quantities) of generated output. Prompts are also a form of programming that can customize the outputs and interactions with an LLM. This paper describes a catalog of prompt engineering techniques presented in pattern form that have been applied to solve common problems when conversing with LLMs. Prompt patterns are a knowledge transfer method analogous to software patterns since they provide reusable solutions to common problems faced in a particular context, i.e., output generation and interaction when working with LLMs. This paper provides the following contributions to research on prompt engineering that apply LLMs to automate software development tasks. First, it provides a framework for documenting patterns for structuring prompts to solve a range of problems so that they can be adapted to different domains. Second, it presents a catalog of patterns that have been applied successfully to improve the outputs of LLM conversations. Third, it explains how prompts can be built from multiple patterns and illustrates prompt patterns that benefit from combination with other prompt patterns.


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Abstract page for arXiv paper 2502.15924v1: Improving Consistency in Large Language Models through Chain of Guidance


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Consistency is a fundamental dimension of trustworthiness in Large Language Models (LLMs). For humans to be able to trust LLM-based applications, their outputs should be consistent when prompted with inputs that carry the same meaning or intent. Despite this need, there is no known mechanism to control and guide LLMs to be more consistent at inference time. In this paper, we introduce a novel alignment strategy to maximize semantic consistency in LLM outputs. Our proposal is based on Chain of Guidance (CoG), a multistep prompting technique that generates highly consistent outputs from LLMs. For closed-book question-answering (Q&A) tasks, when compared to direct prompting, the outputs generated using CoG show improved consistency. While other approaches like template-based responses and majority voting may offer alternative paths to consistency, our work focuses on exploring the potential of guided prompting. We use synthetic data sets comprised of consistent input-output pairs to fine-tune LLMs to produce consistent and correct outputs. Our fine-tuned models are more than twice as consistent compared to base models and show strong generalization capabilities by producing consistent outputs over datasets not used in the fine-tuning process.


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Since the launch of ChatGPT, a powerful AI Chatbot developed by OpenAI, large language models (LLMs) have made significant advancements in both academia and industry, bringing about a fundamental engineering paradigm shift in many areas. While LLMs are powerful, it is also crucial to best use their power where "prompt'' plays a core role. However, the booming LLMs themselves, including excellent APIs like ChatGPT, have several inherent limitations: 1) temporal lag of training data, and 2) the lack of physical capabilities to perform external actions. Recently, we have observed the trend of utilizing prompt-based tools to better utilize the power of LLMs for downstream tasks, but a lack of systematic literature and standardized terminology, partly due to the rapid evolution of this field. Therefore, in this work, we survey related prompting tools and promote the concept of the "Prompting Framework" (PF), i.e. the framework for managing, simplifying, and facilitating interaction with large language models. We define the lifecycle of the PF as a hierarchical structure, from bottom to top, namely: Data Level, Base Level, Execute Level, and Service Level. We also systematically depict the overall landscape of the emerging PF field and discuss potential future research and challenges. To continuously track the developments in this area, we maintain a repository at https://github.com/lxx0628/Prompting-Framework-Survey, which can be a useful resource sharing platform for both academic and industry in this field.


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Abstract page for arXiv paper 2410.21965v1: SG-Bench: Evaluating LLM Safety Generalization Across Diverse Tasks and Prompt Types


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Ensuring the safety of large language model (LLM) applications is essential for developing trustworthy artificial intelligence. Current LLM safety benchmarks have two limitations. First, they focus solely on either discriminative or generative evaluation paradigms while ignoring their interconnection. Second, they rely on standardized inputs, overlooking the effects of widespread prompting techniques, such as system prompts, few-shot demonstrations, and chain-of-thought prompting. To overcome these issues, we developed SG-Bench, a novel benchmark to assess the generalization of LLM safety across various tasks and prompt types. This benchmark integrates both generative and discriminative evaluation tasks and includes extended data to examine the impact of prompt engineering and jailbreak on LLM safety. Our assessment of 3 advanced proprietary LLMs and 10 open-source LLMs with the benchmark reveals that most LLMs perform worse on discriminative tasks than generative ones, and are highly susceptible to prompts, indicating poor generalization in safety alignment. We also explain these findings quantitatively and qualitatively to provide insights for future research.


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Abstract page for arXiv paper 2405.11579v1: Exploring the Capabilities of Prompted Large Language Models in Educational and Assessment Applications


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Exploring the Capabilities of Prompted Large Language Models in Educational and Assessment Applications


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In the era of generative artificial intelligence (AI), the fusion of large language models (LLMs) offers unprecedented opportunities for innovation in the field of modern education. We embark on an exploration of prompted LLMs within the context of educational and assessment applications to uncover their potential. Through a series of carefully crafted research questions, we investigate the effectiveness of prompt-based techniques in generating open-ended questions from school-level textbooks, assess their efficiency in generating open-ended questions from undergraduate-level technical textbooks, and explore the feasibility of employing a chain-of-thought inspired multi-stage prompting approach for language-agnostic multiple-choice question (MCQ) generation. Additionally, we evaluate the ability of prompted LLMs for language learning, exemplified through a case study in the low-resource Indian language Bengali, to explain Bengali grammatical errors. We also evaluate the potential of prompted LLMs to assess human resource (HR) spoken interview transcripts. By juxtaposing the capabilities of LLMs with those of human experts across various educational tasks and domains, our aim is to shed light on the potential and limitations of LLMs in reshaping educational practices.


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Judging the equivalence between two SQL queries is a fundamental problem with many practical applications in data management and SQL generation (i.e., evaluating the quality of generated SQL queries in text-to-SQL task). While the research community has reasoned about SQL equivalence for decades, it poses considerable difficulties and no complete solutions exist. Recently, Large Language Models (LLMs) have shown strong reasoning capability in conversation, question answering and solving mathematics challenges. In this paper, we study if LLMs can be used to determine the equivalence between SQL queries under two notions of SQL equivalence (semantic equivalence and relaxed equivalence). To assist LLMs in generating high quality responses, we present two prompting techniques: Miniature & Mull and Explain & Compare. The former technique is used to evaluate the semantic equivalence in which it asks LLMs to execute a query on a simple database instance and then explore if a counterexample exists by modifying the database. The latter technique is used to evaluate the relaxed equivalence in which it asks LLMs to explain the queries and then compare if they contain significant logical differences. Our experiments demonstrate using our techniques, LLMs is a promising tool to help data engineers in writing semantically equivalent SQL queries, however challenges still persist, and is a better metric for evaluating SQL generation than the popular execution accuracy.


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We propose an algorithm for automatic instruction generation and selection for large language models with human level performance.


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In this post we review advanced prompt engineering techniques like chain of thought (CoT) prompting, self consistency, ReAct, ToT, etc for production grade prompts.


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Learn 26 principles for prompt engineering to increase LLM accuracy by 57% on LLaMA-1/2 (7B, 13B and 70B) and 67% on GPT-3.5/4.


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Abstract page for arXiv paper 2403.03550v1: Emotional Manipulation Through Prompt Engineering Amplifies Disinformation Generation in AI Large Language Models


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Emotional Manipulation Through Prompt Engineering Amplifies Disinformation Generation in AI Large Language Models


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This study investigates the generation of synthetic disinformation by OpenAI's Large Language Models (LLMs) through prompt engineering and explores their responsiveness to emotional prompting. Leveraging various LLM iterations using davinci-002, davinci-003, gpt-3.5-turbo and gpt-4, we designed experiments to assess their success in producing disinformation. Our findings, based on a corpus of 19,800 synthetic disinformation social media posts, reveal that all LLMs by OpenAI can successfully produce disinformation, and that they effectively respond to emotional prompting, indicating their nuanced understanding of emotional cues in text generation. When prompted politely, all examined LLMs consistently generate disinformation at a high frequency. Conversely, when prompted impolitely, the frequency of disinformation production diminishes, as the models often refuse to generate disinformation and instead caution users that the tool is not intended for such purposes. This research contributes to the ongoing discourse surrounding responsible development and application of AI technologies, particularly in mitigating the spread of disinformation and promoting transparency in AI-generated content.


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Abstract page for arXiv paper 2312.08282: Prompting LLMs with content plans to enhance the summarization of scientific articles


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This paper presents novel prompting techniques to improve the performance of automatic summarization systems for scientific articles. Scientific article summarization is highly challenging due to the length and complexity of these documents. We conceive, implement, and evaluate prompting techniques that provide additional contextual information to guide summarization systems. Specifically, we feed summarizers with lists of key terms extracted from articles, such as author keywords or automatically generated keywords. Our techniques are tested with various summarization models and input texts. Results show performance gains, especially for smaller models summarizing sections separately. This evidences that prompting is a promising approach to overcoming the limitations of less powerful systems. Our findings introduce a new research direction of using prompts to aid smaller models.


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In the ever-evolving landscape of artificial intelligence, prompting has emerged as a transformative technique that bridges human intentions and machine understanding. At its core, prompting is the…


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In the ever-evolving landscape of artificial intelligence, prompting has emerged as a transformative technique that bridges human…


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Abstract page for arXiv paper 2501.07815v1: Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models


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Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models


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Recent advances in prompting techniques and multi-agent systems for Large Language Models (LLMs) have produced increasingly complex approaches. However, we lack a framework for characterizing and comparing prompting techniques or understanding their relationship to multi-agent LLM systems. This position paper introduces and explains the concepts of linear contexts (a single, continuous sequence of interactions) and non-linear contexts (branching or multi-path) in LLM systems. These concepts enable the development of an agent-centric projection of prompting techniques, a framework that can reveal deep connections between prompting strategies and multi-agent systems. We propose three conjectures based on this framework: (1) results from non-linear prompting techniques can predict outcomes in equivalent multi-agent systems, (2) multi-agent system architectures can be replicated through single-LLM prompting techniques that simulate equivalent interaction patterns, and (3) these equivalences suggest novel approaches for generating synthetic training data. We argue that this perspective enables systematic cross-pollination of research findings between prompting and multi-agent domains, while providing new directions for improving both the design and training of future LLM systems.


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Abstract page for arXiv paper 2412.20545v1: The Impact of Prompt Programming on Function-Level Code Generation


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Large Language Models (LLMs) are increasingly used by software engineers for code generation. However, limitations of LLMs such as irrelevant or incorrect code have highlighted the need for prompt programming (or prompt engineering) where engineers apply specific prompt techniques (e.g., chain-of-thought or input-output examples) to improve the generated code. Despite this, the impact of different prompt techniques -- and their combinations -- on code generation remains underexplored. In this study, we introduce CodePromptEval, a dataset of 7072 prompts designed to evaluate five prompt techniques (few-shot, persona, chain-of-thought, function signature, list of packages) and their effect on the correctness, similarity, and quality of complete functions generated by three LLMs (GPT-4o, Llama3, and Mistral). Our findings show that while certain prompt techniques significantly influence the generated code, combining multiple techniques does not necessarily improve the outcome. Additionally, we observed a trade-off between correctness and quality when using prompt techniques. Our dataset and replication package enable future research on improving LLM-generated code and evaluating new prompt techniques.


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Abstract page for arXiv paper 2401.14447v1: Wordflow: Social Prompt Engineering for Large Language Models


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Large language models (LLMs) require well-crafted prompts for effective use. Prompt engineering, the process of designing prompts, is challenging, particularly for non-experts who are less familiar with AI technologies. While researchers have proposed techniques and tools to assist LLM users in prompt design, these works primarily target AI application developers rather than non-experts. To address this research gap, we propose social prompt engineering, a novel paradigm that leverages social computing techniques to facilitate collaborative prompt design. To investigate social prompt engineering, we introduce Wordflow, an open-source and social text editor that enables everyday users to easily create, run, share, and discover LLM prompts. Additionally, by leveraging modern web technologies, Wordflow allows users to run LLMs locally and privately in their browsers. Two usage scenarios highlight how social prompt engineering and our tool can enhance laypeople's interaction with LLMs. Wordflow is publicly accessible at https://poloclub.github.io/wordflow.


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With the undeniable rapid development of Conversational Artificial Intelligence (AI) particularly Large Language Models (LLMs), prompt engineering has become an obligatory skill for effective communication and interaction with language driven tools like ChatGPT. It...


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https://media.licdn.com/dms/image/v2/D5612AQHevfUNu_Q4zA/article-cover_image-shrink_720_1280/article-cover_image-shrink_720_1280/0/1736044951418?e=2147483647&v=beta&t=TkvlcLk9MEox369AISyzOw_ATEfR3k3bCem8XoPNmdA


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Latest Prompt Engineering Techniques to Enhance LLM Capabilities: Definitions and Business Examples In the fast-evolving world of artificial intelligence, prompt engineering has become a crucial tool to unlock the full potential of large language models (LLMs). By employing innovative techniques, bu


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Want to make the most out of large language models? Check out these prompting techniques you can start using today.


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This article has explored three promising prompting techniques that have been developed to reduce the occurrence of hallucinations in large language models.


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3 Research-Driven Advanced Prompting Techniques for LLM Efficiency and Speed Optimization - KDnuggets


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https://www.kdnuggets.com/wp-content/uploads/adeleye_3_researchdriven_advanced_prompting_techniques_llm_efficiency_speed_optimization_10.jpg


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https://machinelearningmastery.com/wp-content/uploads/2025/01/mlm-7-next-gen-prompting-techniques.png


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Abstract page for arXiv paper 2406.10430v1: Challenging the Machine: Contestability in Government AI Systems


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In an October 2023 executive order (EO), President Biden issued a detailed but largely aspirational road map for the safe and responsible development and use of artificial intelligence (AI). The challenge for the January 24-25, 2024 workshop was to transform those aspirations regarding one specific but crucial issue -- the ability of individuals to challenge government decisions made about themselves -- into actionable guidance enabling agencies to develop, procure, and use genuinely contestable advanced automated decision-making systems. While the Administration has taken important steps since the October 2023 EO, the insights garnered from our workshop remain highly relevant, as the requirements for contestability of advanced decision-making systems are not yet fully defined or implemented.
  The workshop brought together technologists, members of government agencies and civil society organizations, litigators, and researchers in an intensive two-day meeting that examined the challenges that users, developers, and agencies faced in enabling contestability in light of advanced automated decision-making systems. To ensure a free and open flow of discussion, the meeting was held under a modified version of the Chatham House rule. Participants were free to use any information or details that they learned, but they may not attribute any remarks made at the meeting by the identity or the affiliation of the speaker. Thus, the workshop summary that follows anonymizes speakers and their affiliation. Where an identification of an agency, company, or organization is made, it is done from a public, identified resource and does not necessarily reflect statements made by participants at the workshop.
  This document is a report of that workshop, along with recommendations and explanatory material.


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ADDRESSING AN EMERGENCY SITUATION: The extraordinary threat posed by illegal aliens and drugs, including deadly fentanyl, constitutes a national emergency


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SAFEGUARDING THE NATION: President Donald J. Trump is proceeding with implementing tariffs on Canada and Mexico under the International Emergency Economic


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Abstract page for arXiv paper 2010.10602v2: Will Trump win again in the 2020 election? An answer from a sociophysics model


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This paper predicting Trump victory has been submitted before the election and revised after, allowing to add a Foreword and Note Added in Revision to discuss in details the causes of the failure of the prediction.
  In 2016, Trump was unanimously seen as the loser in the November 8 election. In contrast, using a model of opinion dynamics I have been developing for a few decades within the framework of sociophysics, I predicted his victory against all odds. According to the model, the winning paradoxical martingale of 2016, has been Trump capability to activate frozen prejudices in many voters by provoking their real indignation. However, four year later, Trump shocking outings do not shock anymore, they became devitalized, losing their ability to generate major emotional reactions. Does this mean that this time around he will lose the 2020 election against Biden, as nearly all analysts, pundits and commentators still predict? No, because with frozen prejudices remaining frozen, the spontaneous prejudices will be activated but this time they will benefit to both Biden and Trump. The main ones are the fear of the other candidate policy and the personal stand facing a danger. In addition, Trump presidency having polarized a large part of American voters into narrow-minded anti-Trump and narrow-minded pro-Trump, those I denote in my model as inflexibles, will be driving the dynamics of choices. Both effects, prejudices and inflexibles can either compete or cooperate making their local combination within each state, decisive to determine the faith of the state election. As a result, tiny differences can make the outcome. Based on my rough estimates of associated proportions of inflexibles and prejudices, the model predicts Trump victory in the 2020 November election.


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Abstract page for arXiv paper 2502.02627v1: The Resurgence of Trumponomics: Implications for the Future of ESG Investments in a Changing Political Landscape


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The Resurgence of Trumponomics: Implications for the Future of ESG Investments in a Changing Political Landscape


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Public policy shapes the economic landscape, influencing everything from corporate behavior to individual investment decisions. For Environmental, Social, and Governance (ESG) investors, these policy shifts can create opportunities and challenges as they navigate an ever-changing regulatory environment. The contrast between the Trump and Biden administrations offers a striking example of how differing political agendas can affect ESG investments. Trump's first term was marked by deregulation and policies favoring fossil fuels, which created an uncertain environment for sustainable investments. When Biden assumed office, his focus on climate action and clean energy reinvigorated the ESG sector, offering a more stable and supportive landscape for green investments. However, with Trump's return to power in his second term, these policies are being reversed again, leading to further volatility. This paper explores how such dramatic shifts in public policy influence economic strategies and directly impact ESG investors' decisions, forcing them to constantly reassess their portfolios in response to changing political climates.


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In 2018, the United States imposed Section 232 tariffs on steel and aluminum imports from major trading partners and separately Section 301 tariffs on a broad range of imports from China. In response to these actions, six trading partners—Canada, China, the European Union, India, Mexico, and Turkey—responded with retaliatory tariffs on a range of U.S. agricultural exports, including agricultural and food products. The agricultural products targeted for retaliation were valued at $30.4 billion in 2017, with individual product lines experiencing tariff increases ranging from 2 to 140 percent. This report provides a detailed look at the impact of retaliatory tariffs by State and commodity and estimates the direct export losses associated with the trade conflict.
Morgan, Stephen; Arita, Shawn; Beckman, Jayson; Ahsan, Saquib; Russell, Dylan; Jarrell, Philip; Kenner, Bart


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In 2018, the United States imposed Section 232 tariffs on steel and aluminum imports from major trading partners and separately Section 301 tariffs on a broad range of imports from China. In response to these actions, six trading partners—Canada, China, the European Union, India, Mexico, and Turkey—responded with retaliatory tariffs on a range of U.S. agricultural exports, including agricultural and food products. The agricultural products targeted for retaliation were valued at $30.4 billion in 2017, with individual product lines experiencing tariff increases ranging from 2 to 140 percent. This report provides a detailed look at the impact of retaliatory tariffs by State and commodity and estimates the direct export losses associated with the trade conflict.


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Donald Trump has tweeted thousands of times during his presidency. These public statements are an increasingly important way through which Trump communicates his political and personal views. A better understanding of the way the American public consumes and responds to these tweets is therefore critical. In the present work, we address both consumption of and response to Trump's tweets by studying replies to them on Twitter. With respect to response, we find that a small number of older, white, left-leaning, and female Americans are responsible for the vast majority of replies to Trump's tweets. These individuals also attend to a broader range of Trump's tweets than the rest of the individuals we study. With respect to consumption, we note that Trump's tweets are often viewed not in isolation, but rather in the context of a set of algorithmically-curated replies. These replies may therefore color the way Americans consume Trump's tweets. To this end, we find some evidence that Twitter accounts see replies in line with their political leanings. However, we show that this can be largely, although not entirely, attributed to the fact that Twitter is more likely to show replies by accounts a user follows. As a basis for comparison, all results for Trump are compared and contrasted with replies to Joe Biden's tweets.


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Abstract page for arXiv paper 2008.07301v4: Computational timeline reconstruction of the stories surrounding Trump: Story turbulence, narrative control, and collective chronopathy


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Computational timeline reconstruction of the stories surrounding Trump: Story turbulence, narrative control, and collective chronopathy


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Measuring the specific kind, temporal ordering, diversity, and turnover rate of stories surrounding any given subject is essential to developing a complete reckoning of that subject's historical impact. Here, we use Twitter as a distributed news and opinion aggregation source to identify and track the dynamics of the dominant day-scale stories around Donald Trump, the 45th President of the United States. Working with a data set comprising around 20 billion 1-grams, we first compare each day's 1-gram and 2-gram usage frequencies to those of a year before, to create day- and week-scale timelines for Trump stories for 2016 through 2020. We measure Trump's narrative control, the extent to which stories have been about Trump or put forward by Trump. We then quantify story turbulence and collective chronopathy -- the rate at which a population's stories for a subject seem to change over time. We show that 2017 was the most turbulent overall year for Trump. In 2020, story generation slowed dramatically during the first two major waves of the COVID-19 pandemic, with rapid turnover returning first with the Black Lives Matter protests following George Floyd's murder and then later by events leading up to and following the 2020 US presidential election, including the storming of the US Capitol six days into 2021. Trump story turnover for 2 months during the COVID-19 pandemic was on par with that of 3 days in September 2017. Our methods may be applied to any well-discussed phenomenon, and have potential to enable the computational aspects of journalism, history, and biography.


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Abstract page for arXiv paper 1804.06931v1: Hearts and Politics: Metrics for Tracking Biorhythm Changes during Brexit and Trump


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Our internal experience of time reflects what is going in the world around us. Our body's natural rhythms get disrupted for a variety of external factors, including exposure to collective events. We collect readings of steps, sleep, and heart rates from 11K users of health tracking devices in London and San Francisco. We introduce measures to quantify changes in not only volume of these three bio-signals (as previous research has done) but also synchronicity and periodicity, and we empirically assess how strong those variations are, compared to random expectation, during four major events: Christmas, New Year's Eve, Brexit, and the US presidential election of 2016 (Donald Trump's election). While Christmas and New Year's eve are associated with short-term effects, Brexit and Trump's election are associated with longer-term disruptions. Our results promise to inform the design of new ways of monitoring population health at scale.


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Abstract page for arXiv paper 2401.01405v1: Quantifying the Uniqueness of Donald Trump in Presidential Discourse


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Does Donald Trump speak differently from other presidents? If so, in what ways? Are these differences confined to any single medium of communication? To investigate these questions, this paper introduces a novel metric of uniqueness based on large language models, develops a new lexicon for divisive speech, and presents a framework for comparing the lexical features of political opponents. Applying these tools to a variety of corpora of presidential speeches, we find considerable evidence that Trump's speech patterns diverge from those of all major party nominees for the presidency in recent history. Some notable findings include Trump's employment of particularly divisive and antagonistic language targeting of his political opponents and his patterns of repetition for emphasis. Furthermore, Trump is significantly more distinctive than his fellow Republicans, whose uniqueness values are comparably closer to those of the Democrats. These differences hold across a variety of measurement strategies, arise on both the campaign trail and in official presidential addresses, and do not appear to be an artifact of secular time trends.


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Abstract page for arXiv paper 1701.02182v1: Political elections and uncertainty -Are BRICS markets equally exposed to Trump's agenda?


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There certainly is little or no doubt that politicians, sometimes consciously and sometimes not, exert a significant impact on stock markets. The evolving volatility over the Republican Donald Trump's surprise victory in the US presidential election is a perfect example when politicians, through announced policies, send signals to financial markets. The present paper seeks to address whether BRICS (Brazil, Russia, India, China and South Africa) stock markets equally vulnerable to Trump's plans. For this purpose, two methods were adopted. The first presents an event-study methodology based on regression estimation of abnormal returns. The second is based on vote intentions by integrating data from social media (Twitter), search queries (Google Trends) and public opinion polls. Our results robustly reveal that although some markets emerged losers, others took the opposite route. China took the biggest hit with Brazil, while the damage was much more limited for India and South Africa. These adverse responses can be explained by the Trump's neo-mercantilist attitude revolving around tearing up trade deals, instituting tariffs, and labeling China a "currency manipulator". However, Russia looks to be benefiting due to Trump's sympathetic attitude towards Vladimir Putin and expectations about the scaling down of sanctions imposed on Russia over its role in the conflict in Ukraine.


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Despite troubled trade negotiations, global trade—and trade policy—will thrive in the twenty-first century, but with a bow to the past.Is the multilateral trading order of the twentieth century a historical artifact?Was the creation of the World Trade Organization in 1995 the high point of multilateral cooperation on trade? This new volume, edited by Bernard M. Hoekman and Ernesto Zedillo, assesses the relevance of the WTO in the context of the rise of China and the United States' turn toward unilateral protectionism.The contributors adopt a historical perspective to discuss changes in global trade policy trends, adducing lessons from the past to help understand current trade tensions. Topics include responses to U.S. protectionism under the Trump administration, the policy dimensions of trade in services and the rise of the digital economy, how to strengthen the WTO to better negotiate new rules of the game and adjudicate disputes, managing China's integration into the global trade system, and the implications of global value chains for economic development policies.By reflecting on past episodes of protectionism and how they were resolved, Trade in the 21st Century provides both context and guidance on how trade challenges can be addressed in the coming decades.


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https://books.google.com/books/publisher/content?id=FtqACwAAQBAJ&printsec=frontcover&img=1&zoom=1&edge=curl&imgtk=AFLRE73jSQP2HlvcP59k4pIN2MBWzpnEkTeovth9_fzv7NwlN8S2pBF2GBhjGvMM7YW-wZfgjdPV5nsqmEnKDbBQkCGdE9GV_iukeO4ylwqbf1LxIDMkRchHKYnKZbaJLssbwJWIo6aZ


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    Republican   presidential candidate Donald J. Trump&#39;s sweeping proposals on international   trade, if implemented, could unleash a trade war that would plunge the US   economy into recession and cost more than 4 million private sector American   jobs, according to an empirical analysis of the two candidates&#39; trade agendas   by the Peterson Institute for International Economics. Hillary Clinton, the   Democratic candidate, has expressed skepticism about trade but does not   advocate a change in the status quo. Marcus Noland, Tyler Moran, and Sherman   Robinson employ a macroeconomic model to show that  if Trump raises tariffs sharply on China,   Mexico, and other trading partners, export-dependent US industries in the   information technology, aerospace, and engineering sectors would be the most   severely affected. But the shock resulting from Trump&#39;s proposed trade   sanctions would also damage sectors not engaged directly in trade. In a   separate legal analysis, Gary Clyde Hufbauer argues that there is ample   precedent and scope for a US president to unilaterally raise tariffs as Trump   has vowed to do as a centerpiece of his trade policy. 


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https://books.google.com/books/publisher/content?id=6zMeDQAAQBAJ&printsec=frontcover&img=1&zoom=1&edge=curl&imgtk=AFLRE72xLT3Ed4XFn75PLFoFliqHC76PSlmAeWiK-uLWzy3Bm8yKSz5_oCdS4V8aOuDY-HPuyH-ZOTQiBwZ53xg2r0bllxadBRD1xi07eboRZKqE3rV5BgCFN0_KhnKxKlJbMhGDSVg7


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value:
Provides a roadmap for mature industrialized countries to contribute to and benefit from global trade on new terms.Global trade is heading toward chaos. Globalization has in part been a zero-sum game over the last 20 years, as China&#39;s middle and upper classes have grown sharply while Western economies have stagnated. Wealthy countries, most notably the United States and the United Kingdom, are now on the brink of abandoning free trade as it includes both the principles and the theories behind it because their economies cannot compete with those of China and some developing countries. Prevailing protectionist attitudes and policies are based on short-term thinking and will disappoint future generations. According to author David S. Jacoby, a &quot;new multilateralism&quot; can provide a way out of this impending disaster by preserving innovation and growth while also curbing the impact of countries that manipulate currency, disparage the environment, and violate human rights. Jacoby clearly explains how industrialized nations can compete on a basis of differentiated technology and innovation while letting developing countries compete on a basis of manufacturing, components, and materials and makes a strong case for why the West should recommit to global trade.


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https://books.google.com/books/publisher/content?id=PkrEEAAAQBAJ&printsec=frontcover&img=1&zoom=1&edge=curl&imgtk=AFLRE70kH4yvFx8qJ3ZX_GrEZQDFNyZuUhH7xwn8Odn62tWI-HnR8DjCukoIjmk_-9bZcNIp1LbIwF6Twh7JjETX2GJrjjWvpd5D16p1ALr1YB2C8Bb2KiRGO9x3mUjgfgsGf8IPJkJa


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President Donald Trump signed executive orders Thursday delaying tariffs until April 2 on goods from both Mexico and Canada that are covered by the trade agreement signed between the three countries during his first term.


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https://www.dentons.com/-/media/images/website/background-images/gpof/abstract/adobestock_272437262.ashx


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United States Imposes New Tariffs on Canada, Mexico, and China Citing National Emergency Under The International Emergency Economic Powers Act (IEEPA)


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As of March 4, 2025, all imports from Mexico and Canada are now subject to 25% tariffs (with the exception of Canadian energy imports subject to 10% import duties), and the 10% tariff initially imposed on Chinese imports in February doubling to 20%.


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President Donald Trump espouses “America First” positions which are commonly interpreted as protectionist. However, a closer reading of Donald 


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President Donald Trump espouses “America First” positions which are commonly interpreted as protectionist. However, a closer reading of Donald Trump’s business interests, of his administration’s published trade agenda and of US trade negotiation history calls into question whether “America First” means protectionism. Trump will use large trade deficits to pressure trading partners to further open up their markets. Companies that are successful in exporting to the US market from those countries will be alarmed by protectionist announcements and will therefore most likely pressure their governments to give in to the demands of the Trump administration.


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The Trump administration has intervened in the release of important studies on the bird flu, as an outbreak escalates across the United States.


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Bird flu, or H5N1 avian influenza, has been circulating across North America since 2022, infecting birds, livestock, wildlife, pets, and humans. Despite 70 documented human cases, there are no known instances of human-to-human transmission — but some scientists are starting to worry that it’s only a matter of time.


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https://hms.harvard.edu/sites/default/files/2025-03/750-Bird-Flu-H1N1-53743577158_e581905119_o_credit-CDC-and-NIAID.jpg


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Infectious disease expert Amira Roess answers pressing questions about avian influenza cases reported across the U.S., in light of the first reported human death in Louisiana


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https://gch.gmu.edu/sites/g/files/yyqcgq231/files/styles/large/public/2025-02/250203902.jpg?itok=DwR3t70S


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The AMA Update covers a range of health care topics affecting the lives of physicians and patients. Learn more about the current bird flu outbreak, seasonal flu and the rising measles cases.


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Worst flu season: Influenza virus spread, new bird flu cases in the U.S., plus measles outbreak update


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Binary Search Algorithm is an efficient method for finding a target value in a sorted array by repeatedly halving the search interval, achieving a time complexity of O(log N).


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Binary Search Algorithm, searching algorithm, sorted array, time complexity, iterative Binary Search, recursive Binary Search, Complexity Analysis, Applications of Binary Search, Advantages of Binary Search, Disadvantages of Binary Search, machine learning algorithms, searching in computer graphics, optimal hyperparameters, contiguous memory locations


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Abstract page for arXiv paper 2402.19107v1: Rahmani Sort: A Novel Variant of Insertion Sort Algorithm with O(nlogn) Complexity


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Various decision support systems are available that implement Data Mining and Data Warehousing techniques for diving into the sea of data for getting useful patterns of knowledge (pearls). Classification, regression, clustering, and many other algorithms are used to enhance the precision and accuracy of the decision process. So, there is scope for increasing the response time of the decision process, especially in mission-critical operations. If data are ordered with suitable and efficient sorting operation, the response time of the decision process can be minimized. Insertion sort is much more suitable for such applications due to its simple and straight logic along with its dynamic nature suitable for list implementation. But it is slower than merge sort and quick sort. The main reasons this is slow: firstly, a sequential search is used to find the actual position of the next key element into the sorted left subarray and secondly, shifting of elements is required by one position towards the right for accommodating the newly inserted element. Therefore, I propose a new algorithm by using a novel technique of binary search mechanism for finding the sorted location of the next key item into the previously sorted left subarray much quicker than the conventional insertion sort algorithm. Performance measurement in terms of the actual running time of the new algorithm has been compared with those of other conventional sorting algorithms apart from the insertion sort. The results obtained on various sample data show that the new algorithm is better in performance than the conventional insertion sort and merge sort algorithms.


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A Computer Science portal for geeks. It contains well written, well thought and well explained computer science and programming articles, quizzes and practice/competitive programming/company interview Questions.


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Data Structures, Algorithms, Python, Java, C, C++, JavaScript, Android Development, SQL, Data Science, Machine Learning, PHP, Web Development, System Design, Tutorial, Technical Blogs, Interview Experience, Interview Preparation, Programming, Competitive Programming, Jobs, Coding Contests, GATE CSE, HTML, CSS, React, NodeJS, Placement, Aptitude, Quiz, Computer Science, Programming Examples, GeeksforGeeks Courses, Puzzles, SSC, Banking, UPSC, Commerce, Finance, CBSE, School, k12, General Knowledge, News, Mathematics, Exams


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Implement classic and functional data structures and algorithms using PythonAbout This BookA step by step guide, which will provide you with a thorough discussion on the analysis and design of fundamental Python data structures.Get a better understanding of advanced Python concepts such as big-o notation, dynamic programming, and functional data structures.Explore illustrations to present data structures and algorithms, as well as their analysis, in a clear, visual manner.Who This Book Is ForThe book will appeal to Python developers. A basic knowledge of Python is expected.What You Will LearnGain a solid understanding of Python data structures.Build sophisticated data applications.Understand the common programming patterns and algorithms used in Python data science.Write efficient robust code.In DetailData structures allow you to organize data in a particular way efficiently. They are critical to any problem, provide a complete solution, and act like reusable code. In this book, you will learn the essential Python data structures and the most common algorithms. With this easy-to-read book, you will be able to understand the power of linked lists, double linked lists, and circular linked lists. You will be able to create complex data structures such as graphs, stacks and queues. We will explore the application of binary searches and binary search trees. You will learn the common techniques and structures used in tasks such as preprocessing, modeling, and transforming data. We will also discuss how to organize your code in a manageable, consistent, and extendable way. The book will explore in detail sorting algorithms such as bubble sort, selection sort, insertion sort, and merge sort. By the end of the book, you will learn how to build components that are easy to understand, debug, and use in different applications.Style and ApproachThe easy-to-read book with its fast-paced nature will improve the productivity of Python programmers and improve the performance of Python applications.


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https://books.google.com/books/publisher/content?id=oHc5DwAAQBAJ&printsec=frontcover&img=1&zoom=1&edge=curl&imgtk=AFLRE72khGZtBEvY84qP7SA03exE0J6cPH6wTAU-ju9byld7TxlyuZULvvQxco4S7Q_9Om_dtdC1gpN-4PH7cbGAR0WnE7Y7lXZ2tENfJ6XeO7S29T0W7i7owky_LFIjtjKbawN34vql


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Abstract page for arXiv paper 2406.16729v1: Optimizing Search Strategies: A Study of Two-Pointer Linear Search Implementation


key: og_description
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This report investigates three fundamental search algorithms: Linear Search, Binary Search, and Two Pointer Search. Linear Search checks each element sequentially, Binary Search divides the search space in half, and Two Pointer Search uses two pointers to scan from both ends of a sorted list. We compare these algorithms in terms of time complexity, space complexity, and practical performance. Our findings demonstrate that while Linear Search is straightforward, it is inefficient for large datasets. Binary Search is efficient for sorted data but requires an initial sorting step. The Two Pointer Search, combining elements of both methods, offers a practical balance of simplicity and efficiency. We propose a novel implementation of the Two Pointer Search algorithm and validate its performance through comprehensive testing on various hardware configurations. The results indicate that our proposed algorithm significantly improves search efficiency, making it suitable for both sorted and unsorted datasets. Future work will extend this algorithm to more complex data structures and real-world applications.


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Learn algorithms for solving classic computer science problems with this concise guide covering everything from fundamental algorithms, such as sorting and searching, to modern algorithms used in machine learning and cryptographyKey FeaturesLearn the techniques you need to know to design algorithms for solving complex problemsBecome familiar with neural networks and deep learning techniquesExplore different types of algorithms and choose the right data structures for their optimal implementationBook DescriptionAlgorithms have always played an important role in both the science and practice of computing. Beyond traditional computing, the ability to use algorithms to solve real-world problems is an important skill that any developer or programmer must have. This book will help you not only to develop the skills to select and use an algorithm to solve real-world problems but also to understand how it works. You’ll start with an introduction to algorithms and discover various algorithm design techniques, before exploring how to implement different types of algorithms, such as searching and sorting, with the help of practical examples. As you advance to a more complex set of algorithms, you'll learn about linear programming, page ranking, and graphs, and even work with machine learning algorithms, understanding the math and logic behind them. Further on, case studies such as weather prediction, tweet clustering, and movie recommendation engines will show you how to apply these algorithms optimally. Finally, you’ll become well versed in techniques that enable parallel processing, giving you the ability to use these algorithms for compute-intensive tasks. By the end of this book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms.What you will learnExplore existing data structures and algorithms found in Python librariesImplement graph algorithms for fraud detection using network analysisWork with machine learning algorithms to cluster similar tweets and process Twitter data in real timePredict the weather using supervised learning algorithmsUse neural networks for object detectionCreate a recommendation engine that suggests relevant movies to subscribersImplement foolproof security using symmetric and asymmetric encryption on Google Cloud Platform (GCP)Who this book is forThis book is for programmers or developers who want to understand the use of algorithms for problem-solving and writing efficient code. Whether you are a beginner looking to learn the most commonly used algorithms in a clear and concise way or an experienced programmer looking to explore cutting-edge algorithms in data science, machine learning, and cryptography, you'll find this book useful. Although Python programming experience is a must, knowledge of data science will be helpful but not necessary.


key: og_image
value:
https://books.google.com/books/publisher/content?id=4IzrDwAAQBAJ&printsec=frontcover&img=1&zoom=1&edge=curl&imgtk=AFLRE71GvgSDExjidM42syJQ7IRB4pAeyguExtF_AI0jGWcaXVvSZHvm-QblNpH8DGDa57cUi-z-azDqs_DThCfuKMlBJF-urROEbPdV_K82V1ogLFK-KoaC3QaDmmKsq-Nk9RkdFHbh


key: description
value:
Binary search is a method for searching a sorted list of data to find an item. Here’s how to implement it in Python.


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https://cdn.builtin.com/cdn-cgi/image/f=auto,fit=cover,w=1200,h=635,q=80/https://builtin.com/sites/www.builtin.com/files/2023-02/binary-search-implementation-python.jpg


key: description
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This survey investigates current techniques for representing qualitative data for use as input to neural networks. Techniques for using qualitative data in neural networks are well known. However, researchers continue to discover new variations or entirely new methods for working with categorical data in neural networks. Our primary contribution is to cover these representation techniques in a single work. Practitioners working with big data often have a need to encode categorical values in their datasets in order to leverage machine learning algorithms. Moreover, the size of data sets we consider as big data may cause one to reject some encoding techniques as impractical, due to their running time complexity. Neural networks take vectors of real numbers as inputs. One must use a technique to map qualitative values to numerical values before using them as input to a neural network. These techniques are known as embeddings, encodings, representations, or distributed representations. Another contribution this work makes is to provide references for the source code of various techniques, where we are able to verify the authenticity of the source code. We cover recent research in several domains where researchers use categorical data in neural networks. Some of these domains are natural language processing, fraud detection, and clinical document automation. This study provides a starting point for research in determining which techniques for preparing qualitative data for use with neural networks are best. It is our intention that the reader should use these implementations as a starting point to design experiments to evaluate various techniques for working with qualitative data in neural networks. The third contribution we make in this work is a new perspective on techniques for using categorical data in neural networks. We organize techniques for using categorical data in neural networks into three categories. We find three distinct patterns in techniques that identify a technique as determined, algorithmic, or automated. The fourth contribution we make is to identify several opportunities for future research. The form of the data that one uses as an input to a neural network is crucial for using neural networks effectively. This work is a tool for researchers to find the most effective technique for working with categorical data in neural networks, in big data settings. To the best of our knowledge this is the first in-depth look at techniques for working with categorical data in neural networks.


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https://static-content.springer.com/image/art%3A10.1186%2Fs40537-020-00305-w/MediaObjects/40537_2020_305_Fig1_HTML.png


key: description
value:
Gradient Boosted Decision Trees (GBDT’s) are a powerful tool for classification and regression tasks in Big Data. Researchers should be familiar with the strengths and weaknesses of current implementations of GBDT’s in order to use them effectively and make successful contributions. CatBoost is a member of the family of GBDT machine learning ensemble techniques. Since its debut in late 2018, researchers have successfully used CatBoost for machine learning studies involving Big Data. We take this opportunity to review recent research on CatBoost as it relates to Big Data, and learn best practices from studies that cast CatBoost in a positive light, as well as studies where CatBoost does not outshine other techniques, since we can learn lessons from both types of scenarios. Furthermore, as a Decision Tree based algorithm, CatBoost is well-suited to machine learning tasks involving categorical, heterogeneous data. Recent work across multiple disciplines illustrates CatBoost’s effectiveness and shortcomings in classification and regression tasks. Another important issue we expose in literature on CatBoost is its sensitivity to hyper-parameters and the importance of hyper-parameter tuning. One contribution we make is to take an interdisciplinary approach to cover studies related to CatBoost in a single work. This provides researchers an in-depth understanding to help clarify proper application of CatBoost in solving problems. To the best of our knowledge, this is the first survey that studies all works related to CatBoost in a single publication.


key: og_image
value:
https://static-content.springer.com/image/art%3A10.1186%2Fs40537-020-00369-8/MediaObjects/40537_2020_369_Fig1_HTML.png


key: description
value:
Binary Search is an efficient algorithm for locating an item in a sorted array by repeatedly halving the search interval, achieving a time complexity of O(log N).


key: keywords
value:
Binary Search Algorithm, searching algorithm, sorted array, time complexity, recursive function, iterative binary search, bisect module, search space, middle index, key comparison, duplicate elements, binary search trees, efficient searching, search interval, Python programming


key: description
value:
Learn how to implement binary search in Python using iterative and recursive approaches and explore the bisect module for efficient binary search functions.


key: description
value:
When working with arrays, you’ll often have to search through them to check if they contain a target element. You can always run a sequential search—scanning the array from the beginning to the end—on the array. But if the array is sorted, running th...


key: description
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Explore efficient algorithms in Python with our guide on binary search. Learn how to implement and optimize this fundamental search algorithm for your projects.


key: description
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LEARN HOW TO USE DATA STRUCTURES IN WRITING HIGH PERFORMANCE PYTHON PROGRAMS AND ALGORITHMS This practical introduction to data structures and algorithms can help every programmer who wants to write more efficient software. Building on Robert Lafore's legendary Java-based guide, this book helps you understand exactly how data structures and algorithms operate. You'll learn how to efficiently apply them with the enormously popular Python language and scale your code to handle today's big data challenges. Throughout, the authors focus on real-world examples, communicate key ideas with intuitive, interactive visualizations, and limit complexity and math to what you need to improve performance. Step-by-step, they introduce arrays, sorting, stacks, queues, linked lists, recursion, binary trees, 2-3-4 trees, hash tables, spatial data structures, graphs, and more. Their code examples and illustrations are so clear, you can understand them even if you're a near-beginner, or your experience is with other procedural or object-oriented languages.  Build core computer science skills that take you beyond merely “writing code” Learn how data structures make programs (and programmers) more efficient See how data organization and algorithms affect how much you can do with today's, and tomorrow's, computing resources Develop data structure implementation skills you can use in any language Choose the best data structure(s) and algorithms for each programming problem—and recognize which ones to avoid   Data Structures & Algorithms in Python is packed with examples, review questions, individual and team exercises, thought experiments, and longer programming projects. It's ideal for both self-study and classroom settings, and either as a primary text or as a complement to a more formal presentation.


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https://books.google.com/books/publisher/content?id=dNnPEAAAQBAJ&printsec=frontcover&img=1&zoom=1&edge=curl&imgtk=AFLRE70It6vJloSE5P5D-Dd1-yxxWm7cEyoE75vbSgV5i2arBnUhDtXV3QADnNxf4HOi0kA-w9Z5dKzXEgYTydH7LreU8GNX-4LYD_9sqsXLW7MzIvAsE6GpOFlBdQu_h5hm9H5CrqYr


key: description
value:
Python Algorithms, Second Edition explains the Python approach to algorithm analysis and design. Written by Magnus Lie Hetland, author of Beginning Python, this book is sharply focused on classical algorithms, but it also gives a solid understanding of fundamental algorithmic problem-solving techniques. The book deals with some of the most important and challenging areas of programming and computer science in a highly readable manner. It covers both algorithmic theory and programming practice, demonstrating how theory is reflected in real Python programs. Well-known algorithms and data structures that are built into the Python language are explained, and the user is shown how to implement and evaluate others.


key: og_image
value:
https://books.google.com/books/publisher/content?id=gVInCgAAQBAJ&printsec=frontcover&img=1&zoom=1&edge=curl&imgtk=AFLRE71tHSbUt7k-3RW15ZI-VdARvGsBaYTofgvjh_JqZlhU6EErCzi-an_PgSGyqIHXe_MPi_TjXLIHBSEMhaD8_cuzRM4Fo6jOuUR-ngbQCu0adbcLNEYiVxgA1J2ZE8QX8GAECXkJ


key: description
value:
Learn how to implement the binary search algorithm in Python with step-by-step examples. Understand binary search time complexity, recursive and iterative methods, and real-world use cases to boost your coding skills.


key: og_description
value:
How can I run Windows RT (the restricted ARM version of Windows 8) in an emulator, for development purposes? This question contains two parts:
Obtaining the image: Does an installation image exist...


key: og_description
value:
My question is I have so many columns in my pandas data frame and I am trying to apply the sklearn preprocessing using dataframe mapper from sklearn-pandas library such as 

mapper= DataFrameMapper...


key: og_description
value:
I'm trying to learn how to use QSharedMemory with a simple application. This application should

create a shared memory
write to it
read from it

It is able to create the memory and write to it ove...


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I am learning about yocto build system and just almost a week into it. I randomly chose calcurse Github repository and decided to build it via Yocto recipe so that I can have some practice and I am 


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I have several domains, but not a very large number. I have an apache instance running on Ubuntu 20.04.6 LTS. I would like to clean up my certificates management, but I do not want to break all of my 


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I am developing a Spring Boot application/library where i have a Service that sends a request to a an API. When in integration Test the Entry Point into the Service is an /integration Endpoint. The 


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value:
I am trying to build a prototype application to log &amp; store user activity. For that, I am using GetForegroundWindow() to get present running application title. And I need to save those logs in DB 


key: og_description
value:
Okay, so this is a little hard to explain, but here goes:

I have a dictionary, which I'm adding content to. The content is a hashed username (key) with an IP address (value). 
I was putting the ha...


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Background: Prompt engineering, focusing on crafting effective prompts to large language models (LLMs), has garnered attention for its capabilities at harnessing the potential of LLMs. This is even more crucial in the medical domain due to its specialized terminology and language technicity. Clinical natural language processing applications must navigate complex language and ensure privacy compliance. Prompt engineering offers a novel approach by designing tailored prompts to guide models in exploiting clinically relevant information from complex medical texts. Despite its promise, the efficacy of prompt engineering in the medical domain remains to be fully explored.
Objective: The aim of the study is to review research efforts and technical approaches in prompt engineering for medical applications as well as provide an overview of opportunities and challenges for clinical practice.
Methods: Databases indexing the fields of medicine, computer science, and medical informatics were queried in order to identify relevant published papers. Since prompt engineering is an emerging field, preprint databases were also considered. Multiple data were extracted, such as the prompt paradigm, the involved LLMs, the languages of the study, the domain of the topic, the baselines, and several learning, design, and architecture strategies specific to prompt engineering. We include studies that apply prompt engineering–based methods to the medical domain, published between 2022 and 2024, and covering multiple prompt paradigms such as prompt learning (PL), prompt tuning (PT), and prompt design (PD).
Results: We included 114 recent prompt engineering studies. Among the 3 prompt paradigms, we have observed that PD is the most prevalent (78 papers). In 12 papers, PD, PL, and PT terms were used interchangeably. While ChatGPT is the most commonly used LLM, we have identified 7 studies using this LLM on a sensitive clinical data set. Chain-of-thought, present in 17 studies, emerges as the most frequent PD technique. While PL and PT papers typically provide a baseline for evaluating prompt-based approaches, 61% (48/78) of the PD studies do not report any nonprompt-related baseline. Finally, we individually examine each of the key prompt engineering–specific information reported across papers and find that many studies neglect to explicitly mention them, posing a challenge for advancing prompt engineering research.
Conclusions: In addition to reporting on trends and the scientific landscape of prompt engineering, we provide reporting guidelines for future studies to help advance research in the medical field. We also disclose tables and figures summarizing medical prompt engineering papers available and hope that future contributions will leverage these existing works to better advance the field.


key: keywords
value:
medical informatics; medicine; privacy; computer science; natural language processing; scoping review; medical applications; clinical practice; large language models; nlp; clinical natural language processing; llms; prompt engineering; medical application; medical texts; prompt design; prompt learning; prompt tuning


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Background: Large language models (LLMs) have shown remarkable capabilities in natural language processing (NLP), especially in domains where labeled data are scarce or expensive, such as the clinical domain. However, to unlock the clinical knowledge hidden in these LLMs, we need to design effective prompts that can guide them to perform specific clinical NLP tasks without any task-specific training data. This is known as in-context learning, which is an art and science that requires understanding the strengths and weaknesses of different LLMs and prompt engineering approaches.
Objective: The objective of this study is to assess the effectiveness of various prompt engineering techniques, including 2 newly introduced types—heuristic and ensemble prompts, for zero-shot and few-shot clinical information extraction using pretrained language models.
Methods: This comprehensive experimental study evaluated different prompt types (simple prefix, simple cloze, chain of thought, anticipatory, heuristic, and ensemble) across 5 clinical NLP tasks: clinical sense disambiguation, biomedical evidence extraction, coreference resolution, medication status extraction, and medication attribute extraction. The performance of these prompts was assessed using 3 state-of-the-art language models: GPT-3.5 (OpenAI), Gemini (Google), and LLaMA-2 (Meta). The study contrasted zero-shot with few-shot prompting and explored the effectiveness of ensemble approaches.
Results: The study revealed that task-specific prompt tailoring is vital for the high performance of LLMs for zero-shot clinical NLP. In clinical sense disambiguation, GPT-3.5 achieved an accuracy of 0.96 with heuristic prompts and 0.94 in biomedical evidence extraction. Heuristic prompts, alongside chain of thought prompts, were highly effective across tasks. Few-shot prompting improved performance in complex scenarios, and ensemble approaches capitalized on multiple prompt strengths. GPT-3.5 consistently outperformed Gemini and LLaMA-2 across tasks and prompt types.
Conclusions: This study provides a rigorous evaluation of prompt engineering methodologies and introduces innovative techniques for clinical information extraction, demonstrating the potential of in-context learning in the clinical domain. These findings offer clear guidelines for future prompt-based clinical NLP research, facilitating engagement by non-NLP experts in clinical NLP advancements. To the best of our knowledge, this is one of the first works on the empirical evaluation of different prompt engineering approaches for clinical NLP in this era of generative artificial intelligence, and we hope that it will inspire and inform future research in this area.


key: keywords
value:
evaluation; machine learning; models; clinical information; natural language processing; language; prompts; clinical data; extraction; ensemble; prompt; heuristic; language model; large language model; nlp; gpt; llm; llms; prompt engineering; bard; prompting; in-context learning; zero-shot; few shot; gemini; llama-2


key: og_title
value:
An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing: Algorithm Development and Validation Study


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https://substackcdn.com/image/fetch/w_1200,h_600,c_fill,f_jpg,q_auto:good,fl_progressive:steep,g_auto/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4f77fa47-f998-420e-a4f3-2defeea24374_2124x1194.png


key: description
value:
A look at zero-shot prompting, a technique that enables large language models to perform tasks without explicit training data. Explore its benefits, limitations, best practices, and real-world applications.


key: description
value:
Explore this deep insight on prompt engineering, covering its crucial elements, effective techniques, applications across fields, and best practices for optimal results.


key: description
value:
Explore the nuances of zero-shot and few-shot learning in LLMs, their applications in AI, and useful insights for ML practitioners.


key: og_description
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Check out Anthropic's Model Context Protocol (MCP)—an open standard for data exchange between applications and data sources.


key: description
value:
The Model Context Protocol (MCP) is an open standard introduced by Anthropic in late 2024 to connect AI models with external data sources and tools. It’s essentially a universal interface that lets…


key: description
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Discover what MCP is and how Claude Anthropic's Model Context Protocol revolutionizes AI by connecting models to data sources and tools.


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https://blog.promptlayer.com/content/images/size/w1200/2025/03/How-a-Prompt-Engineering-Tool-Improves-AI-Model-Performance--74-.png


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The Model Context Protocol (MCP) is an open standard for connecting AI assistants to the systems where data lives, including content repositories, business tools, and development environments. Its aim is to help frontier models produce better, more relevant responses.


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https://cdn.sanity.io/images/4zrzovbb/website/1aef864f9b246c740fe3cef6e1068f2220995d5e-2400x1260.png


key: description
value:
This ultimate guide clearly explains the Model Context Protocol (MCP), covering its purpose, benefits, usage, and impact on AI development.


key: og_image
value:
https://mpgone.com/wp-content/uploads/2025/03/epic-cinematic-thumbnail-style-dark-dramatic-back-3.jpg


key: description
value:
Model Context Protocol serves as a critical communication bridge between AI models and external systems, enabling AI assistants to interact directly with...


key: og_description
value:
Use this Windows 10/11 cloud configuration step-by-step setup guide to create your own cloud configuration deployment. You create the Microsoft Entra group and policies using Microsoft Intune, including the enrollment profile, compliance policy, and security baseline. You also deploy apps and resources that users need to do their jobs.


key: og_description
value:
For cloud-native endpoints to access on-premises resources, such as file servers, printers, and web servers, use Windows integrated authentication (WIA) and Microsoft Entra Connect.


key: og_description
value:
Microsoft Intune is a family of on-premises products and cloud services. It includes Intune, Configuration Manager, co-management, Endpoint Analytics, Windows Autopilot, and the admin center to manage cloud devices and on on-premises.


key: og_description
value:
Set up secure cloud-native Windows endpoints that are Microsoft Entra joined, enrolled in Intune, and then deploy at scale with Windows Autopilot.


key: og_description
value:
In the world of IT, an endpoint refers to a computing device or a hardware device connected to a network – this includes computers/workstations, servers, and mobile devices. Endpoints are vulnerable to cyberattacks due to their direct exposure to the internet, potential security misconfigurations, and susceptibility to malware infiltration. This is why it is crucial […]


key: description
value:
My starred Repos. Contribute to fscorrupt/awesome-stars development by creating an account on GitHub.


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value:
https://opengraph.githubassets.com/1d4637b09e6785e980673741bbdaf6f00687f3b12c620dc9240ddfde831ed4d9/fscorrupt/awesome-stars


key: description
value:
Microsoft Endpoint Manager uses Intune and Configuration Manager to remotely configure and secure all of your users’ devices


key: keywords
value:
Parallels RAS alternatives, alternatives to Parallels RAS, best Parallels RAS alternatives, best Parallels RAS alternatives 2025, Parallels RAS competitors


key: description
value:
Compare the best Parallels RAS alternatives in 2025. Explore user reviews, ratings, and pricing of alternatives and competitors to Parallels RAS.


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In this issue: an extensive article with all the news from IBC, and in our test zone we test Elements One and Quales V2.3.14. Enjoy it.


key: description
value:
The Model Context Protocol (MCP) is a standardized protocol that connects AI agents to various external tools and data sources. Connecting an AI system to external tools involves integrating multiple…


key: description
value:
At Open WebUI, we're committed to continually enhancing our platform to provide the best experience for our users. Below, you'll find the structured roadmap for our ongoing and future developments, categorized into Interface, Information Retrieval, and Community.


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value:
The Model Context Protocol (MCP) follows a client-host-server architecture where each host can run multiple client instances. This architecture enables users to integrate AI capabilities across applications while maintaining clear security boundaries and isolating concerns. Built on JSON-RPC, MCP provides a stateful session protocol focused on context exchange and sampling coordination between clients and servers.
Core Components graph LR subgraph "Application Host Process" H[Host] C1[Client 1] C2[Client 2] C3[Client 3] H --> C1 H --> C2 H --> C3 end subgraph "Local machine" S1[Server 1
Files & Git] S2[Server 2
Database] R1[("Local
Resource A")] R2[("Local
Resource B")] C1 --> S1 C2 --> S2 S1 <--> R1 S2 <--> R2 end subgraph "Internet" S3[Server 3
External APIs] R3[("Remote
Resource C")] C3 --> S3 S3 <--> R3 endHost The host process acts as the container and coordinator:


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value:
Discover the most comprehensive and up-to-date collection of MCP servers in the market. This repository serves as a centralized hub, offering an extensive catalog of open-source and proprietary MCP servers, complete with features, documentation links, and contributors. - apappascs/mcp-servers-hub


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value:
https://opengraph.githubassets.com/127b7d7079735b8b5dec46a8d5dac6aa7fa88dc827fa4c6b82204a0615858d36/apappascs/mcp-servers-hub


key: og_image:alt
value:
Discover the most comprehensive and up-to-date collection of MCP servers in the market. This repository serves as a centralized hub, offering an extensive catalog of open-source and proprietary MCP...


key: description
value:
Model Context Protocol (MCP) is an open protocol designed to provide a secure, standardized, and flexible integration between large language models (LLMs) and external data sources or tools. MCP…


key: description
value:
Developed by Anthropic to initially help improve how LLMs and machine learning tools work by making it easier for these systems to talk to other programs. Today, MCP is increasingly being adopted in…


key: og_description
value:
Developed by Anthropic to initially help improve how LLMs and machine learning tools work by making it easier for these systems to talk to…


key: og_url
value:
https://pub.towardsai.net/comprehensive-report-on-model-context-protocol-mcp-with-an-introduction-to-cursor-rules-f2a8a98708e9


key: og_image
value:
https://opengraph.githubassets.com/1185883b9f9cb424a61dde9e2bbd19e4c88784f3a7666fdf29bcbd1709538a8c/open-webui/open-webui/discussions/7363


key: og_image:alt
value:
It would be highly beneficial to integrate support for the Model Context Protocol (MCP) into the Open WebUI project. MCP is a standardized protocol designed to improve interoperability between AI m...


key: description
value:
Explore how the Model Context Protocol (MCP) revolutionizes AI integration with real-time data communication and simplified connectivity.


key: og_image
value:
https://cdn.prod.website-files.com/5e0f1144930a8bc8aace526c/67cd86d0b5402482724bfc00_67cd7c5a4e61f14cbc69eb4a-1741521120693.jpg


key: description
value:
Building AI-powered apps is exciting — until you have to integrate them with external services. Every time your AI needs to query a database, fetch logs from a monitoring tool, or trigger a CI/CD…


key: og_url
value:
https://clubwritter.medium.com/model-context-protocol-mcp-the-concept-of-ai-agent-connectivity-7ec526fd4f8f


key: description
value:
The Model Context Protocol (MCP) is the USB-C for AI, creating a universal standard for seamless AI-data integration. No more custom connectors—just secure, scalable, and efficient AI interactions. Companies like Block and Replit are already leveraging MCP to bridge AI with real-world datasets. Is this the future of AI integration?


key: description
value:
The Model Context Protocol (MCP) is an open standard designed to connect AI assistants with the systems where data actually lives — from content repositories and business tools to development…


key: keywords
value:
Large Language Model,  Efficient Prompting Method,  Automatic Prompt Engineering,  Prompt Compression


key: description
value:
Large Language Models (LLMs) are gaining significant popularity in recent years for specialized tasks using prompts due to their low computational cost. Standard methods like prefix tuning utilize special, modifiable tokens that lack semantic meaning and require extensive training for best performance, often falling short. In this context, we propose a novel method called Semantic Knowledge Tuning (SK-Tuning) for prompt and prefix tuning that employs meaningful words instead of random tokens. This method involves using a fixed LLM to understand and process the semantic content of the prompt through zero-shot capabilities. Following this, it integrates the processed prompt with the input text to improve the model’s performance on particular tasks. Our experimental results show that SK-Tuning exhibits faster training times, fewer parameters, and superior performance on tasks such as text classification and understanding compared to other tuning methods. This approach offers a promising method for optimizing the efficiency and effectiveness of LLMs in processing language tasks.


key: og_title
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Parameter-efficient fine-tuning of large language models using semantic knowledge tuning - Scientific Reports


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https://media.springernature.com/m685/springer-static/image/art%3A10.1038%2Fs41598-024-75599-4/MediaObjects/41598_2024_75599_Fig1_HTML.png


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value:
 Explore the world of prompt engineering with this essential guide. Understand its significance in AI, the role of a prompt engineer, and the future prospects.


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value:
https://media.datacamp.com/legacy/v1692874193/datarhys_an_absurdist_oil_painting_of_a_coder_typing_commands_i_3cc1d847_2b12_4505_abb5_ab38157a0bb5_57b76a9ec1.png


key: description
value:
Prompt engineering refines prompts to obtain more accurate and useful responses from LLMs. Learn more and find prompt examples with Google Cloud. 


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value:
https://opengraph.githubassets.com/bcda2d648622999257407b8e11bfdd5d111575c15d63be344234e6bd7213f536/placeboo/ArxivSummary


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ArxivSummary/data/arxiv/2024-09-30/catalog.csv at 901d2c613a773731c87cbb4962081419541aac2a · placeboo/ArxivSummary


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https://opengraph.githubassets.com/83d109b9a2e5da9ce633b05cc7974e5257b99420d5ae861d6c457c43d79b7f24/kendrick2324/Paper-data-scrape


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ArXiv daily dump and viewer using GitHub Actions - luvata.github.io/arxive - arxive/pages/2025-02-26-cs-cl.html at 9341b486ad647204168aa013996db66c9df8196a · Luvata/arxive


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akitenkrad-blog/src/content/posts/arxiv/202307/20230728000000/index.md at e493935c054a5ba82143a5b8b5a0daeebcafb376 · akitenkrad/akitenkrad-blog


key: description
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Discover effective strategies for prompt engineering to optimize AI-based large language models. Discover how to save time and enhance quality.


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https://synoptekmark.b-cdn.net/wp-content/uploads/2023/07/prompt-engineering-strategies-for-optimizing-ai-language-models.jpg


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arxiv-daily/html/user_0/2023-11-17.html at 1a3074665786adbe2140d325f14a577e8297e535 · wwd29/arxiv-daily


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ArxivPaperReader/docs/source/202404/240409.rst at 090b54f9e0c9c996b4c20f937e1fe5fe4b4384ea · jmzhoulab/ArxivPaperReader


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The designing of prompts has become an indispensable skill necessary for successful interaction with the Large Language Models which include GPT-4 and LLaMA. This article looks at sophisticated…


key: og_description
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The designing of prompts has become an indispensable skill necessary for successful interaction with the Large Language Models which…


key: description
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Unlock AI's potential with prompt engineering. Enhance LLM performance, control, and accuracy. Understand the future of AI interactions.


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ArXiv daily dump and viewer using GitHub Actions - luvata.github.io/arxive - arxive/pages/2024-05-06-cs-ai.html at 9341b486ad647204168aa013996db66c9df8196a · Luvata/arxive


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TOC Prologue Let’s start with the term “prompt”. The term "prompt" can have different meanings depending on the context.


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ArxivSummary/data/arxiv/2024-09-30/catalog.md at 901d2c613a773731c87cbb4962081419541aac2a · placeboo/ArxivSummary


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A simple collection of arXiv daily graph mining-related papers for personal reading. - arXiv-daily-graphMining/info/23-May/19/paper_info.json at 027cdfb86f53a5075e4aed4a4b29a28ba5e51c49 · alexfanjn/arXiv-daily-graphMining


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A simple collection of arXiv daily graph mining-related papers for personal reading. - alexfanjn/arXiv-daily-graphMining


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A simple collection of arXiv daily graph mining-related papers for personal reading. - arXiv-daily-graphMining/info/23-Dec/27/paper_info.json at 027cdfb86f53a5075e4aed4a4b29a28ba5e51c49 · alexfanjn/arXiv-daily-graphMining


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ArxivPaperReader/docs/source/202402/240227.rst at 090b54f9e0c9c996b4c20f937e1fe5fe4b4384ea · jmzhoulab/ArxivPaperReader


key: keywords
value:
Large Language Model,  Natural Language Processing,  Prompt Engineering,  Prompt Engineering Techniques


key: description
value:
Learn about prompt engineering - the practice of designing and optimizing the text prompts given to large language models. Discover how.


key: description
value:
Prompt engineering is a relatively new and evolving field that intersects artificial intelligence, computational linguistics, and user experience design. At its core, prompt engineering is the art…


key: og_description
value:
Prompt engineering is a relatively new and evolving field that intersects artificial intelligence, computational linguistics, and user…


key: description
value:
Abstract page for arXiv paper 2402.14837v1: An Empirical Categorization of Prompting Techniques for Large Language Models: A Practitioner's Guide


key: og_title
value:
An Empirical Categorization of Prompting Techniques for Large Language Models: A Practitioner's Guide


key: og_description
value:
Due to rapid advancements in the development of Large Language Models (LLMs), programming these models with prompts has recently gained significant attention. However, the sheer number of available prompt engineering techniques creates an overwhelming landscape for practitioners looking to utilize these tools. For the most efficient and effective use of LLMs, it is important to compile a comprehensive list of prompting techniques and establish a standardized, interdisciplinary categorization framework. In this survey, we examine some of the most well-known prompting techniques from both academic and practical viewpoints and classify them into seven distinct categories. We present an overview of each category, aiming to clarify their unique contributions and showcase their practical applications in real-world examples in order to equip fellow practitioners with a structured framework for understanding and categorizing prompting techniques tailored to their specific domains. We believe that this approach will help simplify the complex landscape of prompt engineering and enable more effective utilization of LLMs in various applications. By providing practitioners with a systematic approach to prompt categorization, we aim to assist in navigating the intricacies of effective prompt design for conversational pre-trained LLMs and inspire new possibilities in their respective fields.


key: description
value:
Abstract page for arXiv paper 2309.08008v1: An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing


key: og_title
value:
An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing


key: og_description
value:
Large language models (LLMs) have shown remarkable capabilities in Natural Language Processing (NLP), especially in domains where labeled data is scarce or expensive, such as clinical domain. However, to unlock the clinical knowledge hidden in these LLMs, we need to design effective prompts that can guide them to perform specific clinical NLP tasks without any task-specific training data. This is known as in-context learning, which is an art and science that requires understanding the strengths and weaknesses of different LLMs and prompt engineering approaches. In this paper, we present a comprehensive and systematic experimental study on prompt engineering for five clinical NLP tasks: Clinical Sense Disambiguation, Biomedical Evidence Extraction, Coreference Resolution, Medication Status Extraction, and Medication Attribute Extraction. We assessed the prompts proposed in recent literature, including simple prefix, simple cloze, chain of thought, and anticipatory prompts, and introduced two new types of prompts, namely heuristic prompting and ensemble prompting. We evaluated the performance of these prompts on three state-of-the-art LLMs: GPT-3.5, BARD, and LLAMA2. We also contrasted zero-shot prompting with few-shot prompting, and provide novel insights and guidelines for prompt engineering for LLMs in clinical NLP. To the best of our knowledge, this is one of the first works on the empirical evaluation of different prompt engineering approaches for clinical NLP in this era of generative AI, and we hope that it will inspire and inform future research in this area.


key: description
value:
Abstract page for arXiv paper 2402.07927v1: A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications


key: og_description
value:
Prompt engineering has emerged as an indispensable technique for extending the capabilities of large language models (LLMs) and vision-language models (VLMs). This approach leverages task-specific instructions, known as prompts, to enhance model efficacy without modifying the core model parameters. Rather than updating the model parameters, prompts allow seamless integration of pre-trained models into downstream tasks by eliciting desired model behaviors solely based on the given prompt. Prompts can be natural language instructions that provide context to guide the model or learned vector representations that activate relevant knowledge. This burgeoning field has enabled success across various applications, from question-answering to commonsense reasoning. However, there remains a lack of systematic organization and understanding of the diverse prompt engineering methods and techniques. This survey paper addresses the gap by providing a structured overview of recent advancements in prompt engineering, categorized by application area. For each prompting approach, we provide a summary detailing the prompting methodology, its applications, the models involved, and the datasets utilized. We also delve into the strengths and limitations of each approach and include a taxonomy diagram and table summarizing datasets, models, and critical points of each prompting technique. This systematic analysis enables a better understanding of this rapidly developing field and facilitates future research by illuminating open challenges and opportunities for prompt engineering.


key: description
value:
Abstract page for arXiv paper 2501.03508v1: A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models


key: og_description
value:
Designing effective prompts is essential to guiding large language models (LLMs) toward desired responses. Automated prompt engineering aims to reduce reliance on manual effort by streamlining the design, refinement, and optimization of natural language prompts. This paper proposes an optimal learning framework for automated prompt engineering, designed to sequentially identify effective prompt features while efficiently allocating a limited evaluation budget. We introduce a feature-based method to express prompts, which significantly broadens the search space. Bayesian regression is employed to utilize correlations among similar prompts, accelerating the learning process. To efficiently explore the large space of prompt features for a high quality prompt, we adopt the forward-looking Knowledge-Gradient (KG) policy for sequential optimal learning. The KG policy is computed efficiently by solving mixed-integer second-order cone optimization problems, making it scalable and capable of accommodating prompts characterized only through constraints. We demonstrate that our method significantly outperforms a set of benchmark strategies assessed on instruction induction tasks. The results highlight the advantages of using the KG policy for prompt learning given a limited evaluation budget. Our framework provides a solution to deploying automated prompt engineering in a wider range applications where prompt evaluation is costly.


key: description
value:
Abstract page for arXiv paper 2310.06680v1: Benchmarking and Explaining Large Language Model-based Code Generation: A Causality-Centric Approach


key: og_title
value:
Benchmarking and Explaining Large Language Model-based Code Generation: A Causality-Centric Approach


key: og_description
value:
While code generation has been widely used in various software development scenarios, the quality of the generated code is not guaranteed. This has been a particular concern in the era of large language models (LLMs)- based code generation, where LLMs, deemed a complex and powerful black-box model, is instructed by a high-level natural language specification, namely a prompt, to generate code. Nevertheless, effectively evaluating and explaining the code generation capability of LLMs is inherently challenging, given the complexity of LLMs and the lack of transparency.
  Inspired by the recent progress in causality analysis and its application in software engineering, this paper launches a causality analysis-based approach to systematically analyze the causal relations between the LLM input prompts and the generated code. To handle various technical challenges in this study, we first propose a novel causal graph-based representation of the prompt and the generated code, which is established over the fine-grained, human-understandable concepts in the input prompts. The formed causal graph is then used to identify the causal relations between the prompt and the derived code. We illustrate the insights that our framework can provide by studying over 3 popular LLMs with over 12 prompt adjustment strategies. The results of these studies illustrate the potential of our technique to provide insights into LLM effectiveness, and aid end-users in understanding predictions. Additionally, we demonstrate that our approach provides actionable insights to improve the quality of the LLM-generated code by properly calibrating the prompt.


key: og_image
value:
https://media.geeksforgeeks.org/wp-content/uploads/20230605071535/Prompt-Engineering---The-AI-Revolution.webp


key: description
value:
Aman's AI Journal | Course notes and learning material for Artificial Intelligence and Deep Learning Stanford classes.


key: description
value:
Chain-of-thought prompting is a prompt engineering strategy to train language models to handle reasoning tasks. Learn how it works and review examples.


key: description
value:
Abstract page for arXiv paper 2307.12980v1: A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models


key: og_description
value:
Prompt engineering is a technique that involves augmenting a large pre-trained model with task-specific hints, known as prompts, to adapt the model to new tasks. Prompts can be created manually as natural language instructions or generated automatically as either natural language instructions or vector representations. Prompt engineering enables the ability to perform predictions based solely on prompts without updating model parameters, and the easier application of large pre-trained models in real-world tasks. In past years, Prompt engineering has been well-studied in natural language processing. Recently, it has also been intensively studied in vision-language modeling. However, there is currently a lack of a systematic overview of prompt engineering on pre-trained vision-language models. This paper aims to provide a comprehensive survey of cutting-edge research in prompt engineering on three types of vision-language models: multimodal-to-text generation models (e.g. Flamingo), image-text matching models (e.g. CLIP), and text-to-image generation models (e.g. Stable Diffusion). For each type of model, a brief model summary, prompting methods, prompting-based applications, and the corresponding responsibility and integrity issues are summarized and discussed. Furthermore, the commonalities and differences between prompting on vision-language models, language models, and vision models are also discussed. The challenges, future directions, and research opportunities are summarized to foster future research on this topic.


key: description
value:
Abstract page for arXiv paper 2311.09773v1: To be or not to be? an exploration of continuously controllable prompt engineering


key: og_description
value:
As the use of large language models becomes more widespread, techniques like parameter-efficient fine-tuning and other methods for controlled generation are gaining traction for customizing models and managing their outputs. However, the challenge of precisely controlling how prompts influence these models is an area ripe for further investigation. In response, we introduce ControlPE (Continuously Controllable Prompt Engineering). ControlPE enables finer adjustments to prompt effects, complementing existing prompt engineering, and effectively controls continuous targets. This approach harnesses the power of LoRA (Low-Rank Adaptation) to create an effect akin to prompt weighting, enabling fine-tuned adjustments to the impact of prompts. Our methodology involves generating specialized datasets for prompt distillation, incorporating these prompts into the LoRA model, and carefully adjusting LoRA merging weight to regulate the influence of prompts. This provides a dynamic and adaptable tool for prompt control. Through our experiments, we have validated the practicality and efficacy of ControlPE. It proves to be a promising solution for control a variety of prompts, ranging from generating short responses prompts, refusal prompts to chain-of-thought prompts.


key: description
value:
Abstract page for arXiv paper 2503.05070v1: PromptPex: Automatic Test Generation for Language Model Prompts


key: og_description
value:
Large language models (LLMs) are being used in many applications and prompts for these models are integrated into software applications as code-like artifacts. These prompts behave much like traditional software in that they take inputs, generate outputs, and perform some specific function. However, prompts differ from traditional code in many ways and require new approaches to ensure that they are robust. For example, unlike traditional software the output of a prompt depends on the AI model that interprets it. Also, while natural language prompts are easy to modify, the impact of updates is harder to predict. New approaches to testing, debugging, and modifying prompts with respect to the model running them are required.
  To address some of these issues, we developed PromptPex, an LLM-based tool to automatically generate and evaluate unit tests for a given prompt. PromptPex extracts input and output specifications from a prompt and uses them to generate diverse, targeted, and valid unit tests. These tests are instrumental in identifying regressions when a prompt is changed and also serve as a tool to understand how prompts are interpreted by different models. We use PromptPex to generate tests for eight benchmark prompts and evaluate the quality of the generated tests by seeing if they can cause each of four diverse models to produce invalid output. PromptPex consistently creates tests that result in more invalid model outputs than a carefully constructed baseline LLM-based test generator. Furthermore, by extracting concrete specifications from the input prompt, PromptPex allows prompt writers to clearly understand and test specific aspects of their prompts. The source code of PromptPex is available at https://github.com/microsoft/promptpex.


key: description
value:
Abstract page for arXiv paper 2302.12173v2: Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection


key: og_title
value:
Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection


key: og_description
value:
Large Language Models (LLMs) are increasingly being integrated into various applications. The functionalities of recent LLMs can be flexibly modulated via natural language prompts. This renders them susceptible to targeted adversarial prompting, e.g., Prompt Injection (PI) attacks enable attackers to override original instructions and employed controls. So far, it was assumed that the user is directly prompting the LLM. But, what if it is not the user prompting? We argue that LLM-Integrated Applications blur the line between data and instructions. We reveal new attack vectors, using Indirect Prompt Injection, that enable adversaries to remotely (without a direct interface) exploit LLM-integrated applications by strategically injecting prompts into data likely to be retrieved. We derive a comprehensive taxonomy from a computer security perspective to systematically investigate impacts and vulnerabilities, including data theft, worming, information ecosystem contamination, and other novel security risks. We demonstrate our attacks' practical viability against both real-world systems, such as Bing's GPT-4 powered Chat and code-completion engines, and synthetic applications built on GPT-4. We show how processing retrieved prompts can act as arbitrary code execution, manipulate the application's functionality, and control how and if other APIs are called. Despite the increasing integration and reliance on LLMs, effective mitigations of these emerging threats are currently lacking. By raising awareness of these vulnerabilities and providing key insights into their implications, we aim to promote the safe and responsible deployment of these powerful models and the development of robust defenses that protect users and systems from potential attacks.


key: og_description
value:
Prompts are how humans communicate with LLMs. Informative prompts are essential for guiding LLMs to produce the desired output. However, prompt engineering is often tedious and time-consuming, requiring significant expertise, limiting its widespread use. We propose Conversational Prompt Engineering (CPE), a user-friendly tool that helps users create personalized prompts for their specific tasks. CPE uses a chat model to briefly interact with users, helping them articulate their output preferences and integrating these into the prompt. The process includes two main stages: first, the model uses user-provided unlabeled data to generate data-driven questions and utilize user responses to shape the initial instruction. Then, the model shares the outputs generated by the instruction and uses user feedback to further refine the instruction and the outputs. The final result is a few-shot prompt, where the outputs approved by the user serve as few-shot examples. A user study on summarization tasks demonstrates the value of CPE in creating personalized, high-performing prompts. The results suggest that the zero-shot prompt obtained is comparable to its - much longer - few-shot counterpart, indicating significant savings in scenarios involving repetitive tasks with large text volumes.


key: og_description
value:
The increasing reliance on large language models (LLMs) such as ChatGPT in various fields emphasizes the importance of ``prompt engineering,'' a technology to improve the quality of model outputs. With companies investing significantly in expert prompt engineers and educational resources rising to meet market demand, designing high-quality prompts has become an intriguing challenge. In this paper, we propose a novel attack against LLMs, named prompt stealing attacks. Our proposed prompt stealing attack aims to steal these well-designed prompts based on the generated answers. The prompt stealing attack contains two primary modules: the parameter extractor and the prompt reconstruction. The goal of the parameter extractor is to figure out the properties of the original prompts. We first observe that most prompts fall into one of three categories: direct prompt, role-based prompt, and in-context prompt. Our parameter extractor first tries to distinguish the type of prompts based on the generated answers. Then, it can further predict which role or how many contexts are used based on the types of prompts. Following the parameter extractor, the prompt reconstructor can be used to reconstruct the original prompts based on the generated answers and the extracted features. The final goal of the prompt reconstructor is to generate the reversed prompts, which are similar to the original prompts. Our experimental results show the remarkable performance of our proposed attacks. Our proposed attacks add a new dimension to the study of prompt engineering and call for more attention to the security issues on LLMs.


key: description
value:
Abstract page for arXiv paper 2308.11628v1: Prompt Engineering For Students of Medicine and Their Teachers


key: og_description
value:
"Prompt Engineering for Students of Medicine and Their Teachers" brings the principles of prompt engineering for large language models such as ChatGPT and Google Bard to medical education. This book contains a comprehensive guide to prompt engineering to help both teachers and students improve education in the medical field. Just as prompt engineering is critical in getting good information out of an AI, it is also critical to get students to think and understand more deeply. The principles of prompt engineering that we have learned from AI systems have the potential to simultaneously revolutionize learning in the healthcare field. The book analyzes from multiple angles the anatomy of a good prompt for both AI models and students. The different types of prompts are examined, showing how each style has unique characteristics and applications. The principles of prompt engineering, applied properly, are demonstrated to be effective in teaching across the diverse fields of anatomy, physiology, pathology, pharmacology, and clinical skills. Just like ChatGPT and similar large language AI models, students need clear and detailed prompting in order for them to fully understand a topic. Using identical principles, a prompt that gets good information from an AI will also cause a student to think more deeply and accurately. The process of prompt engineering facilitates this process. Because each chapter contains multiple examples and key takeaways, it is a practical guide for implementing prompt engineering in the learning process. It provides a hands-on approach to ensure readers can immediately apply the concepts they learn


key: description
value:
Abstract page for arXiv paper 2407.12994v2: A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks


key: og_description
value:
Large language models (LLMs) have shown remarkable performance on many different Natural Language Processing (NLP) tasks. Prompt engineering plays a key role in adding more to the already existing abilities of LLMs to achieve significant performance gains on various NLP tasks. Prompt engineering requires composing natural language instructions called prompts to elicit knowledge from LLMs in a structured way. Unlike previous state-of-the-art (SoTA) models, prompt engineering does not require extensive parameter re-training or fine-tuning based on the given NLP task and thus solely operates on the embedded knowledge of LLMs. Additionally, LLM enthusiasts can intelligently extract LLMs' knowledge through a basic natural language conversational exchange or prompt engineering, allowing more and more people even without deep mathematical machine learning background to experiment with LLMs. With prompt engineering gaining popularity in the last two years, researchers have come up with numerous engineering techniques around designing prompts to improve accuracy of information extraction from the LLMs. In this paper, we summarize different prompting techniques and club them together based on different NLP tasks that they have been used for. We further granularly highlight the performance of these prompting strategies on various datasets belonging to that NLP task, talk about the corresponding LLMs used, present a taxonomy diagram and discuss the possible SoTA for specific datasets. In total, we read and present a survey of 44 research papers which talk about 39 different prompting methods on 29 different NLP tasks of which most of them have been published in the last two years.


key: description
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Advanced prompting techniques like chain of thought [8] and tree of thought [9] prompting have drastically improved the ability of large language models (LLMs) to solve complex, reasoning-based…


key: og_url
value:
https://medium.com/data-science/graph-based-prompting-and-reasoning-with-language-models-d6acbcd6b3d8


key: description
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Introduction Prompting techniques play a crucial role in enhancing the capabilities of Large Language Models (LLMs) by providing structured inputs that guide their responses. These techniques are designed to elicit specific types of responses from LLMs, ranging from simple translations to complex re


key: og_description
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I will potentially have several USB sound cards connected to a single Raspberry PI and I want to play be able to play sound on speciffic sound card identified by physical USB port it's connected to...


key: og_description
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I'm trying to install Cassandra 4.1.8 on Amazon Linux 3, and it fails due to the below error.
I did install amazon version of java 1.8 - but that doesn't come with the headless package.
Has anyone ...


key: og_description
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How can I make only failed scenarios to be run again automatically on failure ?

Here is some clue on what I am doing:
Pass TestRunner class from command line through cucumber-testng.xml file at run-


key: og_description
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Fromt camera preview is getting mirrored on some devices
I have upgraded the camera package to the latest version (0.11) with Flutter SDK 3.27.4.
Tested on a physical device with the expected result:


key: og_description
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I have a detail TABLE, that I'd like to generate a sub-total for each value in a column.  I have a data table that records a number (key).  I have a reference table that has key/value pairs.  On oc...


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I have some data to plot, which for purposes of organization and annotation are broken into several blocks, defined by a combination of variables including the one I'm calling group in my code. The...


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I plan to create an Oracle database that will contain arrays of numeric data.  Each array has from 2 to 4 dimensions, and on the order of 1000 data points.  I will want to use the arrays in WHERE c...


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So , i created an azure bastion named "test" under the virtual network "RemoteAccess-Bastion-VN".Under this virtual network i also created a subnet "AzureBastionSubnet"  with "/27" range.And when i...


key: og_title
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Auth.js v5 Google login in the production environment redirects to https://localhost after logging in success


key: og_description
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this is my google login action
&quot;use server&quot;;

import { AuthError } from &quot;next-auth&quot;;
import { signIn } from &quot;@/auth&quot;;
export const googleLogin = async () =&gt; {

  t...


key: og_description
value:
I am working on a very big project that blaze persistence is it’s core. We have a very complex architecture that eventually, all db operations relay on a specific blaze query that looks like so:
 r...


