Woodpecker: Hallucination Correction for Multimodal Large Language Models
Paper
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2310.16045
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Published
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14
Hallucination
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HallusionBench: You See What You Think? Or You Think What You See? An Image-Context Reasoning Benchmark Challenging for GPT-4V(ision), LLaVA-1.5, and Other Multi-modality Models
Paper • 2310.14566 • Published • 25 -
MAF: Multi-Aspect Feedback for Improving Reasoning in Large Language Models
Paper • 2310.12426 • Published • 1 -
Corex: Pushing the Boundaries of Complex Reasoning through Multi-Model Collaboration
Paper • 2310.00280 • Published • 3 -
CIEM: Contrastive Instruction Evaluation Method for Better Instruction Tuning
Paper • 2309.02301 • Published • 1 -
Head-to-Tail: How Knowledgeable are Large Language Models (LLM)? A.K.A. Will LLMs Replace Knowledge Graphs?
Paper • 2308.10168 • Published • 2 -
MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Models
Paper • 2308.09729 • Published • 5 -
Natural Logic-guided Autoregressive Multi-hop Document Retrieval for Fact Verification
Paper • 2212.05276 • Published • 1 -
DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models
Paper • 2309.03883 • Published • 33 -
Beyond Words: A Mathematical Framework for Interpreting Large Language Models
Paper • 2311.03033 • Published • 1 -
Fine-tuning Language Models for Factuality
Paper • 2311.08401 • Published • 28 -
Zero-Resource Hallucination Prevention for Large Language Models
Paper • 2309.02654 • Published • 1 -
Hallucination-minimized Data-to-answer Framework for Financial Decision-makers
Paper • 2311.07592 • Published -
Trusting Your Evidence: Hallucinate Less with Context-aware Decoding
Paper • 2305.14739 • Published