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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:2402.13064
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KAN or MLP: A Fairer Comparison
Paper • 2407.16674 • Published • 41 -
MathScale: Scaling Instruction Tuning for Mathematical Reasoning
Paper • 2403.02884 • Published • 15 -
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models
Paper • 2402.13064 • Published • 46 -
DPTDR: Deep Prompt Tuning for Dense Passage Retrieval
Paper • 2208.11503 • Published
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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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DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data
Paper • 2405.14333 • Published • 34 -
Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach
Paper • 2405.15613 • Published • 13 -
DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows
Paper • 2402.10379 • Published • 29 -
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models
Paper • 2402.13064 • Published • 46
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Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling
Paper • 2401.16380 • Published • 48 -
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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Jamba: A Hybrid Transformer-Mamba Language Model
Paper • 2403.19887 • Published • 104 -
sDPO: Don't Use Your Data All at Once
Paper • 2403.19270 • Published • 39 -
ViTAR: Vision Transformer with Any Resolution
Paper • 2403.18361 • Published • 52 -
Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models
Paper • 2403.18814 • Published • 44
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LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement
Paper • 2403.15042 • Published • 25 -
Design2Code: How Far Are We From Automating Front-End Engineering?
Paper • 2403.03163 • Published • 93 -
OS-Copilot: Towards Generalist Computer Agents with Self-Improvement
Paper • 2402.07456 • Published • 41 -
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models
Paper • 2402.13064 • Published • 46
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Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models
Paper • 2402.13064 • Published • 46 -
Textbooks Are All You Need II: phi-1.5 technical report
Paper • 2309.05463 • Published • 87 -
DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows
Paper • 2402.10379 • Published • 29 -
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models
Paper • 2312.06585 • Published • 28