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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
Discover the best community collections!
Collections including paper arxiv:2404.07965
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Rho-1: Not All Tokens Are What You Need
Paper • 2404.07965 • Published • 80 -
VASA-1: Lifelike Audio-Driven Talking Faces Generated in Real Time
Paper • 2404.10667 • Published • 13 -
Instruction-tuned Language Models are Better Knowledge Learners
Paper • 2402.12847 • Published • 24 -
DoRA: Weight-Decomposed Low-Rank Adaptation
Paper • 2402.09353 • Published • 23
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Rho-1: Not All Tokens Are What You Need
Paper • 2404.07965 • Published • 80 -
LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
Paper • 2404.05961 • Published • 62 -
Compression Represents Intelligence Linearly
Paper • 2404.09937 • Published • 27 -
Multi-Head Mixture-of-Experts
Paper • 2404.15045 • Published • 55
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RecurrentGemma: Moving Past Transformers for Efficient Open Language Models
Paper • 2404.07839 • Published • 40 -
Stack More Layers Differently: High-Rank Training Through Low-Rank Updates
Paper • 2307.05695 • Published • 21 -
Rho-1: Not All Tokens Are What You Need
Paper • 2404.07965 • Published • 80 -
Pre-training Small Base LMs with Fewer Tokens
Paper • 2404.08634 • Published • 32
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JetMoE: Reaching Llama2 Performance with 0.1M Dollars
Paper • 2404.07413 • Published • 32 -
Rho-1: Not All Tokens Are What You Need
Paper • 2404.07965 • Published • 80 -
Jamba: A Hybrid Transformer-Mamba Language Model
Paper • 2403.19887 • Published • 99 -
Mixture-of-Depths: Dynamically allocating compute in transformer-based language models
Paper • 2404.02258 • Published • 102