Collection by 
May 8, 2025
Collections
Discover the best community collections!
Collections including paper arxiv:2308.06259
Collection by 
Jul 11, 2025
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Textbooks Are All You Need
Paper • 2306.11644 • Published • 154 -
Textbooks Are All You Need II: phi-1.5 technical report
Paper • 2309.05463 • Published • 89 -
TinyStories: How Small Can Language Models Be and Still Speak Coherent English?
Paper • 2305.07759 • Published • 39 -
Scaling Synthetic Data Creation with 1,000,000,000 Personas
Paper • 2406.20094 • Published • 104
Collection by 
Sep 12, 2024
Collection by 
Feb 22, 2024
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Self-Instruct: Aligning Language Model with Self Generated Instructions
Paper • 2212.10560 • Published • 9 -
Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4
Paper • 2312.16171 • Published • 37 -
DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence
Paper • 2401.14196 • Published • 70 -
AlpaCare:Instruction-tuned Large Language Models for Medical Application
Paper • 2310.14558 • Published • 4
Collection by 
Jun 18, 2024
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AlpaGasus: Training A Better Alpaca with Fewer Data
Paper • 2307.08701 • Published • 24 -
The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset
Paper • 2303.03915 • Published • 7 -
MADLAD-400: A Multilingual And Document-Level Large Audited Dataset
Paper • 2309.04662 • Published • 25 -
SlimPajama-DC: Understanding Data Combinations for LLM Training
Paper • 2309.10818 • Published • 11
Collection by 
Mar 29, 2025
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Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning
Paper • 2211.04325 • Published • 1 -
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 26 -
On the Opportunities and Risks of Foundation Models
Paper • 2108.07258 • Published • 2 -
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
Paper • 2204.07705 • Published • 2
Collection by 
Jun 21, 2024
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Better Synthetic Data by Retrieving and Transforming Existing Datasets
Paper • 2404.14361 • Published • 2 -
Generative AI for Synthetic Data Generation: Methods, Challenges and the Future
Paper • 2403.04190 • Published • 1 -
Best Practices and Lessons Learned on Synthetic Data for Language Models
Paper • 2404.07503 • Published • 31 -
A Multi-Faceted Evaluation Framework for Assessing Synthetic Data Generated by Large Language Models
Paper • 2404.14445 • Published
Collection by 
May 7, 2025
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Large Language Model Alignment: A Survey
Paper • 2309.15025 • Published • 2 -
Aligning Large Language Models with Human: A Survey
Paper • 2307.12966 • Published • 1 -
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
Paper • 2305.18290 • Published • 64 -
SteerLM: Attribute Conditioned SFT as an (User-Steerable) Alternative to RLHF
Paper • 2310.05344 • Published • 1
Collection by Jul 24, 2024
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From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning
Paper • 2308.12032 • Published • 2 -
Know thy corpus! Robust methods for digital curation of Web corpora
Paper • 2003.06389 • Published • 1 -
Self-Alignment with Instruction Backtranslation
Paper • 2308.06259 • Published • 43 -
The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation
Paper • 2305.06156 • Published • 2
Collection by Jul 22, 2024
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Ensemble-Instruct: Generating Instruction-Tuning Data with a Heterogeneous Mixture of LMs
Paper • 2310.13961 • Published • 5 -
Fabricator: An Open Source Toolkit for Generating Labeled Training Data with Teacher LLMs
Paper • 2309.09582 • Published • 4 -
Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models
Paper • 2310.13127 • Published • 12 -
Evaluating the Robustness to Instructions of Large Language Models
Paper • 2308.14306 • Published • 1
Collection by May 8, 2025
Collection by Mar 29, 2025
-
Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning
Paper • 2211.04325 • Published • 1 -
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 26 -
On the Opportunities and Risks of Foundation Models
Paper • 2108.07258 • Published • 2 -
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
Paper • 2204.07705 • Published • 2
Collection by Jul 11, 2025
-
Textbooks Are All You Need
Paper • 2306.11644 • Published • 154 -
Textbooks Are All You Need II: phi-1.5 technical report
Paper • 2309.05463 • Published • 89 -
TinyStories: How Small Can Language Models Be and Still Speak Coherent English?
Paper • 2305.07759 • Published • 39 -
Scaling Synthetic Data Creation with 1,000,000,000 Personas
Paper • 2406.20094 • Published • 104
Collection by Jun 21, 2024
-
Better Synthetic Data by Retrieving and Transforming Existing Datasets
Paper • 2404.14361 • Published • 2 -
Generative AI for Synthetic Data Generation: Methods, Challenges and the Future
Paper • 2403.04190 • Published • 1 -
Best Practices and Lessons Learned on Synthetic Data for Language Models
Paper • 2404.07503 • Published • 31 -
A Multi-Faceted Evaluation Framework for Assessing Synthetic Data Generated by Large Language Models
Paper • 2404.14445 • Published
Collection by Sep 12, 2024
Collection by May 7, 2025
-
Large Language Model Alignment: A Survey
Paper • 2309.15025 • Published • 2 -
Aligning Large Language Models with Human: A Survey
Paper • 2307.12966 • Published • 1 -
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
Paper • 2305.18290 • Published • 64 -
SteerLM: Attribute Conditioned SFT as an (User-Steerable) Alternative to RLHF
Paper • 2310.05344 • Published • 1
Collection by Feb 22, 2024
-
Self-Instruct: Aligning Language Model with Self Generated Instructions
Paper • 2212.10560 • Published • 9 -
Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4
Paper • 2312.16171 • Published • 37 -
DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence
Paper • 2401.14196 • Published • 70 -
AlpaCare:Instruction-tuned Large Language Models for Medical Application
Paper • 2310.14558 • Published • 4
Collection by Jul 24, 2024
-
From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning
Paper • 2308.12032 • Published • 2 -
Know thy corpus! Robust methods for digital curation of Web corpora
Paper • 2003.06389 • Published • 1 -
Self-Alignment with Instruction Backtranslation
Paper • 2308.06259 • Published • 43 -
The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation
Paper • 2305.06156 • Published • 2
Collection by Jun 18, 2024
-
AlpaGasus: Training A Better Alpaca with Fewer Data
Paper • 2307.08701 • Published • 24 -
The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset
Paper • 2303.03915 • Published • 7 -
MADLAD-400: A Multilingual And Document-Level Large Audited Dataset
Paper • 2309.04662 • Published • 25 -
SlimPajama-DC: Understanding Data Combinations for LLM Training
Paper • 2309.10818 • Published • 11
Collection by Jul 22, 2024
-
Ensemble-Instruct: Generating Instruction-Tuning Data with a Heterogeneous Mixture of LMs
Paper • 2310.13961 • Published • 5 -
Fabricator: An Open Source Toolkit for Generating Labeled Training Data with Teacher LLMs
Paper • 2309.09582 • Published • 4 -
Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models
Paper • 2310.13127 • Published • 12 -
Evaluating the Robustness to Instructions of Large Language Models
Paper • 2308.14306 • Published • 1