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Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs
Paper • 2404.15676 • Published -
How faithful are RAG models? Quantifying the tug-of-war between RAG and LLMs' internal prior
Paper • 2404.10198 • Published • 8 -
RAFT: Adapting Language Model to Domain Specific RAG
Paper • 2403.10131 • Published • 72 -
FaaF: Facts as a Function for the evaluation of RAG systems
Paper • 2403.03888 • Published
Collections
Discover the best community collections!
Collections including paper arxiv:2505.04921
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EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 29 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 14 -
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Paper • 2402.04615 • Published • 44 -
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
Paper • 2402.05008 • Published • 23
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Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs
Paper • 2404.15676 • Published -
How faithful are RAG models? Quantifying the tug-of-war between RAG and LLMs' internal prior
Paper • 2404.10198 • Published • 8 -
RAFT: Adapting Language Model to Domain Specific RAG
Paper • 2403.10131 • Published • 72 -
FaaF: Facts as a Function for the evaluation of RAG systems
Paper • 2403.03888 • Published
-
EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 29 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 14 -
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Paper • 2402.04615 • Published • 44 -
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
Paper • 2402.05008 • Published • 23