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Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 22 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 82 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 145 -
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity
Paper • 2401.17072 • Published • 25
Collections
Discover the best community collections!
Collections including paper arxiv:2410.12705
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iVideoGPT: Interactive VideoGPTs are Scalable World Models
Paper • 2405.15223 • Published • 12 -
Meteor: Mamba-based Traversal of Rationale for Large Language and Vision Models
Paper • 2405.15574 • Published • 53 -
An Introduction to Vision-Language Modeling
Paper • 2405.17247 • Published • 87 -
Matryoshka Multimodal Models
Paper • 2405.17430 • Published • 31
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The Impact of Hyperparameters on Large Language Model Inference Performance: An Evaluation of vLLM and HuggingFace Pipelines
Paper • 2408.01050 • Published • 8 -
Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
Paper • 2408.03314 • Published • 54 -
Towards a Unified View of Preference Learning for Large Language Models: A Survey
Paper • 2409.02795 • Published • 72 -
Paper Copilot: A Self-Evolving and Efficient LLM System for Personalized Academic Assistance
Paper • 2409.04593 • Published • 24
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PDFTriage: Question Answering over Long, Structured Documents
Paper • 2309.08872 • Published • 53 -
Adapting Large Language Models via Reading Comprehension
Paper • 2309.09530 • Published • 77 -
Table-GPT: Table-tuned GPT for Diverse Table Tasks
Paper • 2310.09263 • Published • 39 -
Context-Aware Meta-Learning
Paper • 2310.10971 • Published • 16
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EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 25 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 12 -
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Paper • 2402.04615 • Published • 40 -
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
Paper • 2402.05008 • Published • 20