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Collections including paper arxiv:2404.05961
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Rho-1: Not All Tokens Are What You Need
Paper • 2404.07965 • Published • 89 -
LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
Paper • 2404.05961 • Published • 65 -
Compression Represents Intelligence Linearly
Paper • 2404.09937 • Published • 27 -
Multi-Head Mixture-of-Experts
Paper • 2404.15045 • Published • 60
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A Mechanistic Understanding of Alignment Algorithms: A Case Study on DPO and Toxicity
Paper • 2401.01967 • Published -
Secrets of RLHF in Large Language Models Part I: PPO
Paper • 2307.04964 • Published • 28 -
Zephyr: Direct Distillation of LM Alignment
Paper • 2310.16944 • Published • 123 -
LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
Paper • 2404.05961 • Published • 65
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LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
Paper • 2404.05961 • Published • 65 -
Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention
Paper • 2404.07143 • Published • 106 -
Scaling (Down) CLIP: A Comprehensive Analysis of Data, Architecture, and Training Strategies
Paper • 2404.08197 • Published • 28 -
Pre-training Small Base LMs with Fewer Tokens
Paper • 2404.08634 • Published • 35
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LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
Paper • 2404.05961 • Published • 65 -
OmniFusion Technical Report
Paper • 2404.06212 • Published • 75 -
Adapting LLaMA Decoder to Vision Transformer
Paper • 2404.06773 • Published • 18 -
BRAVE: Broadening the visual encoding of vision-language models
Paper • 2404.07204 • Published • 19
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LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
Paper • 2404.05961 • Published • 65 -
Ferret-UI: Grounded Mobile UI Understanding with Multimodal LLMs
Paper • 2404.05719 • Published • 83 -
CodeEditorBench: Evaluating Code Editing Capability of Large Language Models
Paper • 2404.03543 • Published • 16 -
Are large language models superhuman chemists?
Paper • 2404.01475 • Published • 17