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Collections including paper arxiv:2404.07965

Self-Rewarding Language Models

Paper • 2401.10020 • Published Jan 18 • 143
Orion-14B: Open-source Multilingual Large Language Models

Paper • 2401.12246 • Published Jan 20 • 11
MambaByte: Token-free Selective State Space Model

Paper • 2401.13660 • Published Jan 24 • 50
MM-LLMs: Recent Advances in MultiModal Large Language Models

Paper • 2401.13601 • Published Jan 24 • 44

Addition is All You Need for Energy-efficient Language Models

Paper • 2410.00907 • Published Oct 1 • 143
Emu3: Next-Token Prediction is All You Need

Paper • 2409.18869 • Published Sep 27 • 89
An accurate detection is not all you need to combat label noise in web-noisy datasets

Paper • 2407.05528 • Published Jul 8 • 3
Is It Really Long Context if All You Need Is Retrieval? Towards Genuinely Difficult Long Context NLP

Paper • 2407.00402 • Published Jun 29 • 22

Associative Recurrent Memory Transformer

Paper • 2407.04841 • Published Jul 5 • 31
Mixture-of-Agents Enhances Large Language Model Capabilities

Paper • 2406.04692 • Published Jun 7 • 55
Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Paper • 2405.21060 • Published May 31 • 63
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Paper • 2404.14219 • Published Apr 22 • 251

Rho-1: Not All Tokens Are What You Need

Paper • 2404.07965 • Published Apr 11 • 84
VASA-1: Lifelike Audio-Driven Talking Faces Generated in Real Time

Paper • 2404.10667 • Published Apr 16 • 17
Instruction-tuned Language Models are Better Knowledge Learners

Paper • 2402.12847 • Published Feb 20 • 24
DoRA: Weight-Decomposed Low-Rank Adaptation

Paper • 2402.09353 • Published Feb 14 • 26

Rho-1: Not All Tokens Are What You Need

Paper • 2404.07965 • Published Apr 11 • 84

Rho-1: Not All Tokens Are What You Need

Paper • 2404.07965 • Published Apr 11 • 84
Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models

Paper • 2402.14714 • Published Feb 22 • 4

Interesting Papers

Rho-1: Not All Tokens Are What You Need

Paper • 2404.07965 • Published Apr 11 • 84
LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Paper • 2404.05961 • Published Apr 9 • 64
Compression Represents Intelligence Linearly

Paper • 2404.09937 • Published Apr 15 • 27
Multi-Head Mixture-of-Experts

Paper • 2404.15045 • Published Apr 23 • 59

Papers - Fine-tuning - Math

Rho-1: Not All Tokens Are What You Need

Paper • 2404.07965 • Published Apr 11 • 84

Papers - Selective Language Modeling vs Causal

Rho-1: Not All Tokens Are What You Need

Paper • 2404.07965 • Published Apr 11 • 84

Papers - University of Xiamen

Rho-1: Not All Tokens Are What You Need

Paper • 2404.07965 • Published Apr 11 • 84

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