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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 • 79 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 143 -
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity
Paper • 2401.17072 • Published • 25
Collections
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Collections including paper arxiv:2404.07965
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Self-Rewarding Language Models
Paper • 2401.10020 • Published • 143 -
Orion-14B: Open-source Multilingual Large Language Models
Paper • 2401.12246 • Published • 10 -
MambaByte: Token-free Selective State Space Model
Paper • 2401.13660 • Published • 49 -
MM-LLMs: Recent Advances in MultiModal Large Language Models
Paper • 2401.13601 • Published • 44
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MADLAD-400: A Multilingual And Document-Level Large Audited Dataset
Paper • 2309.04662 • Published • 22 -
Neurons in Large Language Models: Dead, N-gram, Positional
Paper • 2309.04827 • Published • 16 -
Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs
Paper • 2309.05516 • Published • 9 -
DrugChat: Towards Enabling ChatGPT-Like Capabilities on Drug Molecule Graphs
Paper • 2309.03907 • Published • 8
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Addition is All You Need for Energy-efficient Language Models
Paper • 2410.00907 • Published • 138 -
Emu3: Next-Token Prediction is All You Need
Paper • 2409.18869 • Published • 84 -
An accurate detection is not all you need to combat label noise in web-noisy datasets
Paper • 2407.05528 • Published • 3 -
Is It Really Long Context if All You Need Is Retrieval? Towards Genuinely Difficult Long Context NLP
Paper • 2407.00402 • Published • 22
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Associative Recurrent Memory Transformer
Paper • 2407.04841 • Published • 31 -
Mixture-of-Agents Enhances Large Language Model Capabilities
Paper • 2406.04692 • Published • 55 -
Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality
Paper • 2405.21060 • Published • 63 -
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
Paper • 2404.14219 • Published • 251
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Rho-1: Not All Tokens Are What You Need
Paper • 2404.07965 • Published • 84 -
VASA-1: Lifelike Audio-Driven Talking Faces Generated in Real Time
Paper • 2404.10667 • Published • 15 -
Instruction-tuned Language Models are Better Knowledge Learners
Paper • 2402.12847 • Published • 24 -
DoRA: Weight-Decomposed Low-Rank Adaptation
Paper • 2402.09353 • Published • 25
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Rho-1: Not All Tokens Are What You Need
Paper • 2404.07965 • Published • 84 -
LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
Paper • 2404.05961 • Published • 64 -
Compression Represents Intelligence Linearly
Paper • 2404.09937 • Published • 27 -
Multi-Head Mixture-of-Experts
Paper • 2404.15045 • Published • 59