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Textbooks Are All You Need
Paper • 2306.11644 • Published • 142 -
Textbooks Are All You Need II: phi-1.5 technical report
Paper • 2309.05463 • Published • 87 -
TinyStories: How Small Can Language Models Be and Still Speak Coherent English?
Paper • 2305.07759 • Published • 33 -
Scaling Synthetic Data Creation with 1,000,000,000 Personas
Paper • 2406.20094 • Published • 94
Collections
Discover the best community collections!
Collections including paper arxiv:2404.07503
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Better Synthetic Data by Retrieving and Transforming Existing Datasets
Paper • 2404.14361 • Published • 1 -
Generative AI for Synthetic Data Generation: Methods, Challenges and the Future
Paper • 2403.04190 • Published -
Best Practices and Lessons Learned on Synthetic Data for Language Models
Paper • 2404.07503 • Published • 29 -
A Multi-Faceted Evaluation Framework for Assessing Synthetic Data Generated by Large Language Models
Paper • 2404.14445 • Published
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A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models
Paper • 2406.11289 • Published • 5 -
Best Practices and Lessons Learned on Synthetic Data for Language Models
Paper • 2404.07503 • Published • 29 -
Spectra: A Comprehensive Study of Ternary, Quantized, and FP16 Language Models
Paper • 2407.12327 • Published • 77 -
Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges
Paper • 2408.08946 • Published • 10
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Best Practices and Lessons Learned on Synthetic Data for Language Models
Paper • 2404.07503 • Published • 29 -
Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences
Paper • 2404.03715 • Published • 60 -
Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing
Paper • 2406.08464 • Published • 65 -
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models
Paper • 2402.13064 • Published • 46
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A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity
Paper • 2305.13169 • Published • 3 -
A Survey on Data Selection for Language Models
Paper • 2402.16827 • Published • 4 -
HuggingFaceFW/fineweb-edu
Viewer • Updated • 3B • 568k • 530 -
allenai/MADLAD-400
Updated • 44.1k • 125
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Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling
Paper • 2401.16380 • Published • 47 -
Best Practices and Lessons Learned on Synthetic Data for Language Models
Paper • 2404.07503 • Published • 29 -
WizardLM: Empowering Large Language Models to Follow Complex Instructions
Paper • 2304.12244 • Published • 13 -
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models
Paper • 2402.13064 • Published • 46
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Self-Rewarding Language Models
Paper • 2401.10020 • Published • 143 -
Self-Discover: Large Language Models Self-Compose Reasoning Structures
Paper • 2402.03620 • Published • 109 -
OS-Copilot: Towards Generalist Computer Agents with Self-Improvement
Paper • 2402.07456 • Published • 41 -
Learning From Mistakes Makes LLM Better Reasoner
Paper • 2310.20689 • Published • 28
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Best Practices and Lessons Learned on Synthetic Data for Language Models
Paper • 2404.07503 • Published • 29 -
Better Synthetic Data by Retrieving and Transforming Existing Datasets
Paper • 2404.14361 • Published • 1 -
Source2Synth: Synthetic Data Generation and Curation Grounded in Real Data Sources
Paper • 2409.08239 • Published • 16