Textbooks Are All You Need
Paper • 2306.11644 • Published • 159
Synthetic Data Generation
SDG papers
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Textbooks Are All You Need II: phi-1.5 technical report
Paper • 2309.05463 • Published • 92 -
TinyStories: How Small Can Language Models Be and Still Speak Coherent English?
Paper • 2305.07759 • Published • 47 -
Scaling Synthetic Data Creation with 1,000,000,000 Personas
Paper • 2406.20094 • Published • 107 -
Instruction Pre-Training: Language Models are Supervised Multitask Learners
Paper • 2406.14491 • Published • 96 -
Improving Text Embeddings with Large Language Models
Paper • 2401.00368 • Published • 85 -
Enhancing Chat Language Models by Scaling High-quality Instructional Conversations
Paper • 2305.14233 • Published • 8 -
Magicoder: Source Code Is All You Need
Paper • 2312.02120 • Published • 83 -
Adapting Large Language Models via Reading Comprehension
Paper • 2309.09530 • Published • 82 -
Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models
Paper • 2401.01335 • Published • 69 -
Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing
Paper • 2406.08464 • Published • 72 -
WaveCoder: Widespread And Versatile Enhanced Instruction Tuning with Refined Data Generation
Paper • 2312.14187 • Published • 50 -
Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling
Paper • 2401.16380 • Published • 53 -
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models
Paper • 2402.13064 • Published • 52 -
AgentInstruct: Toward Generative Teaching with Agentic Flows
Paper • 2407.03502 • Published • 51 -
Toward General Instruction-Following Alignment for Retrieval-Augmented Generation
Paper • 2410.09584 • Published • 48 -
Self-Alignment with Instruction Backtranslation
Paper • 2308.06259 • Published • 43 -
OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset
Paper • 2402.10176 • Published • 38 -
DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows
Paper • 2402.10379 • Published • 31 -
Best Practices and Lessons Learned on Synthetic Data for Language Models
Paper • 2404.07503 • Published • 32 -
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models
Paper • 2312.06585 • Published • 29 -
Becoming self-instruct: introducing early stopping criteria for minimal instruct tuning
Paper • 2307.03692 • Published • 27 -
AlpaGasus: Training A Better Alpaca with Fewer Data
Paper • 2307.08701 • Published • 24 -
Simple synthetic data reduces sycophancy in large language models
Paper • 2308.03958 • Published • 23 -
CodecLM: Aligning Language Models with Tailored Synthetic Data
Paper • 2404.05875 • Published • 18 -
Source2Synth: Synthetic Data Generation and Curation Grounded in Real Data Sources
Paper • 2409.08239 • Published • 21 -
WizardLM: Empowering Large Language Models to Follow Complex Instructions
Paper • 2304.12244 • Published • 14 -
Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation
Paper • 2402.18334 • Published • 12 -
Synthesizing Text-to-SQL Data from Weak and Strong LLMs
Paper • 2408.03256 • Published • 10 -
Self-Instruct: Aligning Language Model with Self Generated Instructions
Paper • 2212.10560 • Published • 9 -
Ensemble-Instruct: Generating Instruction-Tuning Data with a Heterogeneous Mixture of LMs
Paper • 2310.13961 • Published • 5 -
STaR: Bootstrapping Reasoning With Reasoning
Paper • 2203.14465 • Published • 9 -
M2Lingual: Enhancing Multilingual, Multi-Turn Instruction Alignment in Large Language Models
Paper • 2406.16783 • Published • 4 -
Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations
Paper • 2310.07849 • Published • 2 -
Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration
Paper • 2310.09168 • Published • 2 -
Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions
Paper • 2306.04140 • Published • 2 -
SALMON: Self-Alignment with Principle-Following Reward Models
Paper • 2310.05910 • Published • 2 -
Better Synthetic Data by Retrieving and Transforming Existing Datasets
Paper • 2404.14361 • Published • 2 -
Impossible Distillation: from Low-Quality Model to High-Quality Dataset & Model for Summarization and Paraphrasing
Paper • 2305.16635 • Published • 1 -
Arena Learning: Build Data Flywheel for LLMs Post-training via Simulated Chatbot Arena
Paper • 2407.10627 • Published -
ZeroGen: Efficient Zero-shot Learning via Dataset Generation
Paper • 2202.07922 • Published • 1 -
West-of-N: Synthetic Preference Generation for Improved Reward Modeling
Paper • 2401.12086 • Published • 1 -
Automatic Instruction Evolving for Large Language Models
Paper • 2406.00770 • Published • 3 -
Generative AI for Synthetic Data Generation: Methods, Challenges and the Future
Paper • 2403.04190 • Published • 1 -
On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey
Paper • 2406.15126 • Published • 1 -
Large Language Models for Data Annotation: A Survey
Paper • 2402.13446 • Published • 1 -
Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias
Paper • 2306.15895 • Published -
A Multi-Faceted Evaluation Framework for Assessing Synthetic Data Generated by Large Language Models
Paper • 2404.14445 • Published -
TarGEN: Targeted Data Generation with Large Language Models
Paper • 2310.17876 • Published -
#InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language Models
Paper • 2308.07074 • Published -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 156 -
Orca 2: Teaching Small Language Models How to Reason
Paper • 2311.11045 • Published • 78 -
Orca: Progressive Learning from Complex Explanation Traces of GPT-4
Paper • 2306.02707 • Published • 51 -
WizardCoder: Empowering Code Large Language Models with Evol-Instruct
Paper • 2306.08568 • Published • 34 -
LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset
Paper • 2309.11998 • Published • 27 -
Let's Synthesize Step by Step: Iterative Dataset Synthesis with Large Language Models by Extrapolating Errors from Small Models
Paper • 2310.13671 • Published • 19 -
Self-play with Execution Feedback: Improving Instruction-following Capabilities of Large Language Models
Paper • 2406.13542 • Published • 17 -
Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models
Paper • 2310.13127 • Published • 12 -
WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct
Paper • 2308.09583 • Published • 8 -
GenQA: Generating Millions of Instructions from a Handful of Prompts
Paper • 2406.10323 • Published • 5 -
UltraFeedback: Boosting Language Models with High-quality Feedback
Paper • 2310.01377 • Published • 6 -
Model Dementia: Generated Data Makes Models Forget
Paper • 2305.17493 • Published • 7 -
Large Language Model as a User Simulator
Paper • 2308.11534 • Published • 2 -
Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor
Paper • 2212.09689 • Published • 1 -
Aligning Large Language Models through Synthetic Feedback
Paper • 2305.13735 • Published • 1 -
Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision
Paper • 2305.03047 • Published • 1 -
Mixture of Soft Prompts for Controllable Data Generation
Paper • 2303.01580 • Published • 1 -
Refined Direct Preference Optimization with Synthetic Data for Behavioral Alignment of LLMs
Paper • 2402.08005 • Published • 1 -
Harnessing the Power of David against Goliath: Exploring Instruction Data Generation without Using Closed-Source Models
Paper • 2308.12711 • Published • 1 -
Generating Training Data with Language Models: Towards Zero-Shot Language Understanding
Paper • 2202.04538 • Published -
Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models
Paper • 2311.00287 • Published -
GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation
Paper • 2104.08826 • Published -
Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models
Paper • 2302.00618 • Published -
MIND: Math Informed syNthetic Dialogues for Pretraining LLMs
Paper • 2410.12881 • Published • 2 -
LAB: Large-Scale Alignment for ChatBots
Paper • 2403.01081 • Published -
Large Language Models Can Self-Improve
Paper • 2210.11610 • Published -
Dynosaur: A Dynamic Growth Paradigm for Instruction-Tuning Data Curation
Paper • 2305.14327 • Published -
Automatically Generating Numerous Context-Driven SFT Data for LLMs across Diverse Granularity
Paper • 2405.16579 • Published -
Data Augmentation using Pre-trained Transformer Models
Paper • 2003.02245 • Published -
Unsupervised Neural Machine Translation with Generative Language Models Only
Paper • 2110.05448 • Published -
Instruction Tuning with GPT-4
Paper • 2304.03277 • Published • 1 -
Content preserving text generation with attribute controls
Paper • 1811.01135 • Published -
Large Language Models Are Human-Level Prompt Engineers
Paper • 2211.01910 • Published • 1 -
XPersona: Evaluating Multilingual Personalized Chatbot
Paper • 2003.07568 • Published -
PersonaMath: Enhancing Math Reasoning through Persona-Driven Data Augmentation
Paper • 2410.01504 • Published -
Do Not Worry if You Do Not Have Data: Building Pretrained Language Models Using Translationese
Paper • 2403.13638 • Published • 1