awesome-llm-synthetic-data
github.com/wasiahmad/awesome-llm-synthetic-data ↗A reading list on LLM based Synthetic Data Generation 🔥
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1. Surveys2. Methods3. Application Areas4. Datasets5. Tools6. Blogs
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5. Tools
- AgentInstruct: Toward Generative Teaching with Agentic Flows
- DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows
- Distilabel: An AI Feedback (AIF) Framework for Building Datasets with and for LLMs
- Fuxion: Synthetic Data Generation and Normalization Functions using Langchain + LLMs
1. Surveys
- A Survey on Bridging VLMs and Synthetic Data
- Best Practices and Lessons Learned on Synthetic Data for Language Models
- Comprehensive Exploration of Synthetic Data Generation: A Survey
- Generative AI for Synthetic Data Generation: Methods, Challenges and the Future
- Large Language Models for Data Annotation: A Survey
- On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey
3. Application Areas
- Augmenting Math Word Problems via Iterative Question Composing3.1. Mathematical Reasoning
- AutoCoder: Enhancing Code Large Language Model with AIEV-Instruct3.2. Code Generation
- CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning3.2. Code Generation
- Constitutional AI: Harmlessness from AI Feedback3.4. Alignment
- Distilling LLMs' Decomposition Abilities into Compact Language Models3.1. Mathematical Reasoning
- DyCodeEval: Dynamic Benchmarking of Reasoning Capabilities in Code Large Language Models Under Data Contamination3.2. Code Generation
2. Methods
- Automatic Instruction Evolving for Large Language Models2.1. Techniques
- CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society2.1. Techniques
- CodecLM: Aligning Language Models with Tailored Synthetic Data2.2. Instruction Generation with High Quality/Complexity
- Generating Training Data with Language Models: Towards Zero-Shot Language Understanding2.1. Techniques
- Instruction Pre-Training:Language Models are Supervised Multitask Learners2.1. Techniques
- Large Language Models Can Self-Improve2.1. Techniques
4. Datasets
- Code Alpaca: An Instruction-following LLaMA Model trained on code generation instructions
- Open Artificial Knowledge
- Synthetic-Text-To-SQL: A synthetic dataset for training language models to generate SQL queries from natural language prompts
- SynthPAI: A Synthetic Dataset for Personal Attribute Inference
Showing a sample of 85 resources. View the full list on GitHub →