This repository serves as a curated reading list on the topic of synthetic data generation for Large Language Models (LLMs). It aims to provide researchers and practitioners with a comprehensive overview of papers, tools, and blogs related to creating and utilizing synthetic data, particularly data generated by LLMs for LLMs. The benefit is a centralized resource for understanding the state-of-the-art in this rapidly evolving field.

How It Works

The repository categorizes resources into surveys, methods (techniques, instruction generation), application areas (reasoning, code, SQL, alignment, etc.), datasets, tools, and blogs. It highlights key papers and techniques such as STaR for reasoning bootstrapping, Self-Instruct for instruction generation, and Constitutional AI for alignment, showcasing diverse approaches to synthetic data creation and its application across various LLM tasks.

Quick Start & Requirements

This is a curated list of resources, not a runnable software project. No installation or specific requirements are needed to browse the content.

Highlighted Details

• Extensive coverage of synthetic data generation techniques, including self-instruction, self-play, and instruction evolution.
• Detailed sections on application areas, such as mathematical reasoning, code generation, and alignment, with relevant papers.
• Inclusion of specific datasets and tools designed for synthetic data generation and LLM workflows.
• Links to numerous academic papers from top-tier conferences (NeurIPS, ICLR, ACL, etc.) and arXiv.

Maintenance & Community

The repository is actively maintained, with recent commits and a welcome stance towards Pull Requests. It is a community-driven effort, indicated by the "Awesome" badge and the call for PRs.

Licensing & Compatibility

The repository itself is licensed under the MIT License, allowing for broad use and modification. The linked papers and tools will have their own respective licenses.

Limitations & Caveats

As a reading list, it does not provide executable code or direct tools for synthetic data generation. Users must refer to the individual papers and tools for their specific requirements and implementations.