-
Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 20 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 76 -
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
Discover the best community collections!
Collections including paper arxiv:2403.08763
-
The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs
Paper • 2210.14986 • Published • 4 -
Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2
Paper • 2311.10702 • Published • 17 -
Large Language Models as Optimizers
Paper • 2309.03409 • Published • 72 -
From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting
Paper • 2309.04269 • Published • 28
-
Unlocking the conversion of Web Screenshots into HTML Code with the WebSight Dataset
Paper • 2403.09029 • Published • 53 -
LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression
Paper • 2403.12968 • Published • 20 -
RAFT: Adapting Language Model to Domain Specific RAG
Paper • 2403.10131 • Published • 64 -
Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking
Paper • 2403.09629 • Published • 54
-
Simple and Scalable Strategies to Continually Pre-train Large Language Models
Paper • 2403.08763 • Published • 48 -
Jamba: A Hybrid Transformer-Mamba Language Model
Paper • 2403.19887 • Published • 99 -
Transformer-Lite: High-efficiency Deployment of Large Language Models on Mobile Phone GPUs
Paper • 2403.20041 • Published • 34 -
Advancing LLM Reasoning Generalists with Preference Trees
Paper • 2404.02078 • Published • 41