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Woodpecker: Hallucination Correction for Multimodal Large Language Models
Paper • 2310.16045 • Published • 14 -
SILC: Improving Vision Language Pretraining with Self-Distillation
Paper • 2310.13355 • Published • 7 -
To See is to Believe: Prompting GPT-4V for Better Visual Instruction Tuning
Paper • 2311.07574 • Published • 14 -
MyVLM: Personalizing VLMs for User-Specific Queries
Paper • 2403.14599 • Published • 15
Collections
Discover the best community collections!
Collections including paper arxiv:2310.16045
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Improved Baselines with Visual Instruction Tuning
Paper • 2310.03744 • Published • 37 -
DeepSeek-VL: Towards Real-World Vision-Language Understanding
Paper • 2403.05525 • Published • 39 -
Qwen-VL: A Frontier Large Vision-Language Model with Versatile Abilities
Paper • 2308.12966 • Published • 7 -
LLaVA-Gemma: Accelerating Multimodal Foundation Models with a Compact Language Model
Paper • 2404.01331 • Published • 25
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AtP*: An efficient and scalable method for localizing LLM behaviour to components
Paper • 2403.00745 • Published • 11 -
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 602 -
MobiLlama: Towards Accurate and Lightweight Fully Transparent GPT
Paper • 2402.16840 • Published • 23 -
LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens
Paper • 2402.13753 • Published • 112
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Deep reinforcement learning from human preferences
Paper • 1706.03741 • Published • 3 -
Training language models to follow instructions with human feedback
Paper • 2203.02155 • Published • 16 -
Direct Preference-based Policy Optimization without Reward Modeling
Paper • 2301.12842 • Published -
Woodpecker: Hallucination Correction for Multimodal Large Language Models
Paper • 2310.16045 • Published • 14
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Woodpecker: Hallucination Correction for Multimodal Large Language Models
Paper • 2310.16045 • Published • 14 -
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
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Woodpecker: Hallucination Correction for Multimodal Large Language Models
Paper • 2310.16045 • Published • 14 -
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 -
SILC: Improving Vision Language Pretraining with Self-Distillation
Paper • 2310.13355 • Published • 7 -
Conditional Diffusion Distillation
Paper • 2310.01407 • Published • 20