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Collections including paper arxiv:2305.18290
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Direct Preference Optimization: Your Language Model is Secretly a Reward Model
Paper • 2305.18290 • Published • 53 -
Towards Efficient and Exact Optimization of Language Model Alignment
Paper • 2402.00856 • Published -
A General Theoretical Paradigm to Understand Learning from Human Preferences
Paper • 2310.12036 • Published • 13 -
Statistical Rejection Sampling Improves Preference Optimization
Paper • 2309.06657 • Published • 14
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KTO: Model Alignment as Prospect Theoretic Optimization
Paper • 2402.01306 • Published • 16 -
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
Paper • 2305.18290 • Published • 53 -
SimPO: Simple Preference Optimization with a Reference-Free Reward
Paper • 2405.14734 • Published • 11 -
Anchored Preference Optimization and Contrastive Revisions: Addressing Underspecification in Alignment
Paper • 2408.06266 • Published • 10
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Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles
Paper • 2306.00989 • Published • 1 -
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
Paper • 2305.18290 • Published • 53 -
Scalable Diffusion Models with Transformers
Paper • 2212.09748 • Published • 18 -
Matryoshka Representation Learning
Paper • 2205.13147 • Published • 11
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STaR: Bootstrapping Reasoning With Reasoning
Paper • 2203.14465 • Published • 8 -
DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models
Paper • 2401.06066 • Published • 49 -
DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
Paper • 2405.04434 • Published • 17 -
Prompt Cache: Modular Attention Reuse for Low-Latency Inference
Paper • 2311.04934 • Published • 32
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Mamba: Linear-Time Sequence Modeling with Selective State Spaces
Paper • 2312.00752 • Published • 140 -
Elucidating the Design Space of Diffusion-Based Generative Models
Paper • 2206.00364 • Published • 15 -
GLU Variants Improve Transformer
Paper • 2002.05202 • Published • 2 -
StarCoder 2 and The Stack v2: The Next Generation
Paper • 2402.19173 • Published • 138
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Instruction Pre-Training: Language Models are Supervised Multitask Learners
Paper • 2406.14491 • Published • 89 -
Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality
Paper • 2405.21060 • Published • 64 -
Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Models
Paper • 2405.20541 • Published • 22 -
MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark
Paper • 2406.01574 • Published • 45
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Attention Is All You Need
Paper • 1706.03762 • Published • 53 -
Language Models are Few-Shot Learners
Paper • 2005.14165 • Published • 13 -
GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints
Paper • 2305.13245 • Published • 5 -
Llama 2: Open Foundation and Fine-Tuned Chat Models
Paper • 2307.09288 • Published • 244