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Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 21 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 78 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 140 -
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:2403.18802
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LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression
Paper • 2403.12968 • Published • 24 -
PERL: Parameter Efficient Reinforcement Learning from Human Feedback
Paper • 2403.10704 • Published • 56 -
Alignment Studio: Aligning Large Language Models to Particular Contextual Regulations
Paper • 2403.09704 • Published • 30 -
RAFT: Adapting Language Model to Domain Specific RAG
Paper • 2403.10131 • Published • 65
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Long-form factuality in large language models
Paper • 2403.18802 • Published • 23 -
Attention Is All You Need
Paper • 1706.03762 • Published • 41 -
Language Models are Few-Shot Learners
Paper • 2005.14165 • Published • 11 -
A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4
Paper • 2310.12321 • Published • 1
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BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text
Paper • 2403.18421 • Published • 21 -
Long-form factuality in large language models
Paper • 2403.18802 • Published • 23 -
stanford-crfm/BioMedLM
Text Generation • Updated • 2.64k • 389 -
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
Paper • 2305.18290 • Published • 43