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Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 20 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 75 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 135 -
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity
Paper • 2401.17072 • Published • 22
Collections
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Collections including paper arxiv:2405.09818
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EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 21 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 9 -
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Paper • 2402.04615 • Published • 33 -
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
Paper • 2402.05008 • Published • 19
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Needle In A Multimodal Haystack
Paper • 2406.07230 • Published • 51 -
OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text
Paper • 2406.08418 • Published • 28 -
Chameleon: Mixed-Modal Early-Fusion Foundation Models
Paper • 2405.09818 • Published • 110 -
MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training
Paper • 2403.09611 • Published • 123
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RLHF Workflow: From Reward Modeling to Online RLHF
Paper • 2405.07863 • Published • 62 -
Chameleon: Mixed-Modal Early-Fusion Foundation Models
Paper • 2405.09818 • Published • 110 -
Meteor: Mamba-based Traversal of Rationale for Large Language and Vision Models
Paper • 2405.15574 • Published • 52 -
An Introduction to Vision-Language Modeling
Paper • 2405.17247 • Published • 77