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RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented Generation
Paper • 2408.02545 • Published • 39 -
RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering
Paper • 2407.13998 • Published -
RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation
Paper • 2408.08067 • Published • 1 -
RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning
Paper • 2504.20073 • Published • 12
Collections
Discover the best community collections!
Collections including paper arxiv:2406.04744
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Instruction Pre-Training: Language Models are Supervised Multitask Learners
Paper • 2406.14491 • Published • 95 -
Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality
Paper • 2405.21060 • Published • 67 -
Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Models
Paper • 2405.20541 • Published • 24 -
MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark
Paper • 2406.01574 • Published • 51
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Mixture-of-Agents Enhances Large Language Model Capabilities
Paper • 2406.04692 • Published • 60 -
CRAG -- Comprehensive RAG Benchmark
Paper • 2406.04744 • Published • 48 -
Boosting Large-scale Parallel Training Efficiency with C4: A Communication-Driven Approach
Paper • 2406.04594 • Published • 8 -
Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models
Paper • 2406.04271 • Published • 30
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Bootstrapping Language Models with DPO Implicit Rewards
Paper • 2406.09760 • Published • 41 -
DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence
Paper • 2406.11931 • Published • 67 -
Prism: A Framework for Decoupling and Assessing the Capabilities of VLMs
Paper • 2406.14544 • Published • 35 -
Instruction Pre-Training: Language Models are Supervised Multitask Learners
Paper • 2406.14491 • Published • 95
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Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models
Paper • 2402.14848 • Published • 20 -
The Prompt Report: A Systematic Survey of Prompting Techniques
Paper • 2406.06608 • Published • 68 -
CRAG -- Comprehensive RAG Benchmark
Paper • 2406.04744 • Published • 48 -
Transformers meet Neural Algorithmic Reasoners
Paper • 2406.09308 • Published • 44
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Husky: A Unified, Open-Source Language Agent for Multi-Step Reasoning
Paper • 2406.06469 • Published • 29 -
Mixture-of-Agents Enhances Large Language Model Capabilities
Paper • 2406.04692 • Published • 60 -
CRAG -- Comprehensive RAG Benchmark
Paper • 2406.04744 • Published • 48 -
ShareGPT4Video: Improving Video Understanding and Generation with Better Captions
Paper • 2406.04325 • Published • 75
-
RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented Generation
Paper • 2408.02545 • Published • 39 -
RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering
Paper • 2407.13998 • Published -
RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation
Paper • 2408.08067 • Published • 1 -
RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning
Paper • 2504.20073 • Published • 12
-
Instruction Pre-Training: Language Models are Supervised Multitask Learners
Paper • 2406.14491 • Published • 95 -
Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality
Paper • 2405.21060 • Published • 67 -
Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Models
Paper • 2405.20541 • Published • 24 -
MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark
Paper • 2406.01574 • Published • 51
-
Bootstrapping Language Models with DPO Implicit Rewards
Paper • 2406.09760 • Published • 41 -
DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence
Paper • 2406.11931 • Published • 67 -
Prism: A Framework for Decoupling and Assessing the Capabilities of VLMs
Paper • 2406.14544 • Published • 35 -
Instruction Pre-Training: Language Models are Supervised Multitask Learners
Paper • 2406.14491 • Published • 95
-
Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models
Paper • 2402.14848 • Published • 20 -
The Prompt Report: A Systematic Survey of Prompting Techniques
Paper • 2406.06608 • Published • 68 -
CRAG -- Comprehensive RAG Benchmark
Paper • 2406.04744 • Published • 48 -
Transformers meet Neural Algorithmic Reasoners
Paper • 2406.09308 • Published • 44
-
Mixture-of-Agents Enhances Large Language Model Capabilities
Paper • 2406.04692 • Published • 60 -
CRAG -- Comprehensive RAG Benchmark
Paper • 2406.04744 • Published • 48 -
Boosting Large-scale Parallel Training Efficiency with C4: A Communication-Driven Approach
Paper • 2406.04594 • Published • 8 -
Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models
Paper • 2406.04271 • Published • 30
-
Husky: A Unified, Open-Source Language Agent for Multi-Step Reasoning
Paper • 2406.06469 • Published • 29 -
Mixture-of-Agents Enhances Large Language Model Capabilities
Paper • 2406.04692 • Published • 60 -
CRAG -- Comprehensive RAG Benchmark
Paper • 2406.04744 • Published • 48 -
ShareGPT4Video: Improving Video Understanding and Generation with Better Captions
Paper • 2406.04325 • Published • 75