9/ MedGraphRAG – a graph-based framework for the medical domain with a focus on enhancing LLMs and generating evidence-based results; leverages a hybrid static-semantic approach to chunk documents to improve context capture; entities and medical knowledge are represented through
LLMS
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NL2SQL Techniques Survey: LLM Models and Evaluation Methods
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10/ Survey of NL2QL – a comprehensive overview of NL2SQL techniques powered by LLMs; covers models, data collection, evaluation methods, and error analysis.
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rStar Enhances Small Language Models Reasoning Without Fine-tuning
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7/ rStar – introduces self-play mutual reasoning to improve the reasoning capabilities of small language models without fine-tuning or superior models; MCTS is augmented with human-like reasoning actions, obtained from SLMs, to build richer reasoning trajectories…
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Optimal Scaling of LLM Inference-Time Compute
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8/ Scaling LLM Test-Time Compute Optimally – investigates the scaling behaviors of inference-time computation in LLMs; in particular, it analyses how much an LLM can be improved provided a fixed amount of inference-time compute; finds that the effectiveness of different scaling
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RAGChecker: Fine-Grained RAG Evaluation Framework
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5/ RAGChecker – a fine-grained evaluation framework for diagnosing retrieval and generation modules in RAG; shows that RAGChecker has better correlations with human judgment; reports several revealing insightful patterns and trade-offs in design choices of RAG architectures.
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HybridRAG Combines GraphRAG VectorRAG for Superior Performance
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6/ HybirdRAG – combines GraphRAG and VectorRAG leading to a HybridRAG system that outperforms both individually; it was tested on a set of financial earning call transcripts. Combining the advantages of both approaches provides more accurate answers to queries.
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LongWriter AgentWrite Enables LLMs Generate Beyond 20K Words
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3/ LongWriter – proposes AgentWrite to enable off-the-shelf LLMs to generate coherent outputs beyond 20K words; AgentWrite breaks down the long generation task into subtasks and in a divide-and-conquer approach generates…
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EfficientRAG: Auto-Encoder LM for Intelligent Chunk Retrieval
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4/ EfficientRAG – trains an auto-encoder LM to label and tag chunks; it retrieves relevant chunks, tags them as either or , and annotates chunks for continuous processing; then a filter model is trained to formulate the next-hop query based on the
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Grok-2: New Frontier Model Surpasses Claude and GPT-4
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2/ Grok-2 – a new frontier model with strong code, math, and reasoning capabilities which includes a large and small model; outperforms both Claude 3.5 Sonnet and GPT-4-Turbo on the LMSYS Chatbot Arena; claims to improve capabilities including instruction following, retrieval,