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Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models
Paper • 2402.13064 • Published • 50 -
DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows
Paper • 2402.10379 • Published • 31 -
Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach
Paper • 2405.15613 • Published • 17 -
Are You Sure? Rank Them Again: Repeated Ranking For Better Preference Datasets
Paper • 2405.18952 • Published • 10
Collections
Discover the best community collections!
Collections including paper arxiv:2406.08464
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Self-Rewarding Language Models
Paper • 2401.10020 • Published • 151 -
Orion-14B: Open-source Multilingual Large Language Models
Paper • 2401.12246 • Published • 14 -
MambaByte: Token-free Selective State Space Model
Paper • 2401.13660 • Published • 60 -
MM-LLMs: Recent Advances in MultiModal Large Language Models
Paper • 2401.13601 • Published • 48
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In-Context Pretraining: Language Modeling Beyond Document Boundaries
Paper • 2310.10638 • Published • 30 -
Magicoder: Source Code Is All You Need
Paper • 2312.02120 • Published • 82 -
Parameter Efficient Tuning Allows Scalable Personalization of LLMs for Text Entry: A Case Study on Abbreviation Expansion
Paper • 2312.14327 • Published • 8 -
WaveCoder: Widespread And Versatile Enhanced Instruction Tuning with Refined Data Generation
Paper • 2312.14187 • Published • 50
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PRDP: Proximal Reward Difference Prediction for Large-Scale Reward Finetuning of Diffusion Models
Paper • 2402.08714 • Published • 15 -
Data Engineering for Scaling Language Models to 128K Context
Paper • 2402.10171 • Published • 25 -
RLVF: Learning from Verbal Feedback without Overgeneralization
Paper • 2402.10893 • Published • 12 -
Coercing LLMs to do and reveal (almost) anything
Paper • 2402.14020 • Published • 13
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A Picture is Worth More Than 77 Text Tokens: Evaluating CLIP-Style Models on Dense Captions
Paper • 2312.08578 • Published • 20 -
ZeroQuant(4+2): Redefining LLMs Quantization with a New FP6-Centric Strategy for Diverse Generative Tasks
Paper • 2312.08583 • Published • 11 -
Vision-Language Models as a Source of Rewards
Paper • 2312.09187 • Published • 14 -
StemGen: A music generation model that listens
Paper • 2312.08723 • Published • 49
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Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models
Paper • 2402.13064 • Published • 50 -
DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows
Paper • 2402.10379 • Published • 31 -
Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach
Paper • 2405.15613 • Published • 17 -
Are You Sure? Rank Them Again: Repeated Ranking For Better Preference Datasets
Paper • 2405.18952 • Published • 10
-
PRDP: Proximal Reward Difference Prediction for Large-Scale Reward Finetuning of Diffusion Models
Paper • 2402.08714 • Published • 15 -
Data Engineering for Scaling Language Models to 128K Context
Paper • 2402.10171 • Published • 25 -
RLVF: Learning from Verbal Feedback without Overgeneralization
Paper • 2402.10893 • Published • 12 -
Coercing LLMs to do and reveal (almost) anything
Paper • 2402.14020 • Published • 13
-
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 151 -
Orion-14B: Open-source Multilingual Large Language Models
Paper • 2401.12246 • Published • 14 -
MambaByte: Token-free Selective State Space Model
Paper • 2401.13660 • Published • 60 -
MM-LLMs: Recent Advances in MultiModal Large Language Models
Paper • 2401.13601 • Published • 48
-
A Picture is Worth More Than 77 Text Tokens: Evaluating CLIP-Style Models on Dense Captions
Paper • 2312.08578 • Published • 20 -
ZeroQuant(4+2): Redefining LLMs Quantization with a New FP6-Centric Strategy for Diverse Generative Tasks
Paper • 2312.08583 • Published • 11 -
Vision-Language Models as a Source of Rewards
Paper • 2312.09187 • Published • 14 -
StemGen: A music generation model that listens
Paper • 2312.08723 • Published • 49
-
In-Context Pretraining: Language Modeling Beyond Document Boundaries
Paper • 2310.10638 • Published • 30 -
Magicoder: Source Code Is All You Need
Paper • 2312.02120 • Published • 82 -
Parameter Efficient Tuning Allows Scalable Personalization of LLMs for Text Entry: A Case Study on Abbreviation Expansion
Paper • 2312.14327 • Published • 8 -
WaveCoder: Widespread And Versatile Enhanced Instruction Tuning with Refined Data Generation
Paper • 2312.14187 • Published • 50