44 lines
2.9 KiB
Plaintext
44 lines
2.9 KiB
Plaintext
Criteria for Paper Selection:
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When evaluating paper titles and abstracts, please consider the following criteria. Prioritize inclusiveness to reduce the risk of missing potentially interesting research.
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Papers must meet ALL of these criteria:
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1. Practical Applications: Does the paper focus on real-world or practical applications of Large Language Models (LLMs), particularly in areas like knowledge graphs, retrieval-augmented generation (RAG), or agentic AI?
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2. Experimental Results and Quantitative Metrics: Does the paper include experimental results with quantitative metrics that demonstrate performance improvements or innovative applications? Look for concrete results related to areas like prompt engineering.
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3. Comparison with State-of-the-Art: Does the paper compare its results or methodologies with existing state-of-the-art techniques, and does it demonstrate any advancements in terms of performance, scalability, or efficiency?
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Papers should meet at least one of these criteria:
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4. Methodology and Implementation Details: Does the paper clearly describe its methodology and implementation, particularly how LLMs are utilized in practical or real-world tasks?
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5. Real-world Applications and Challenges: Does the paper discuss real-world applications or address limitations and challenges involving LLMs, especially in autonomous or agentic AI scenarios?
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Additional Considerations:
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- Novelty: Does the paper introduce novel approaches or techniques, especially those extending the application or integration of LLMs with technologies like knowledge graphs?
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- Is the paper's approach implementable with current standard tools?
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- Agentic AI: Does the paper describe applications where LLMs enable AI to autonomously perform complex, real-world tasks beyond sandboxed interactions?
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- Reproducibility and Documentation: Are there sufficient details to support reproducibility and transparent documentation of the methodologies and results?
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- Impact through Experimental Validation: Does the paper show robust experimental validation, with datasets and methods that closely reflect real-world scenarios?
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The paper should be REJECTED if it:
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- Primarily focuses on medical applications of AI
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- Primarily focuses on social applications of AI in regard to Diversity, Social harm, or similar issues.
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- Primarily focuses on video processing
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- Primarily focuses on responsible AI application or AI ethics
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- Primarily focuses on law, either with AI as subject or participant.
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Instructions:
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- Analyze each paper's title and abstract to determine how many criteria are met.
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- Prioritize inclusiveness: Favor selection over rejection if a paper seems marginally relevant.
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- Use a simple scoring system: A paper meeting at least two or three criteria should be accepted for further review.
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Please make a best-guess decision based on the information provided in the titles and abstracts. Err on the side of potential inclusion to capture a broad spectrum of relevant research.
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