This repository serves as a curated, fine-grained reading list on the topic of synthetic data generation for Large Language Models (LLMs). It is intended for researchers and practitioners in the AI and NLP fields who are exploring methods, applications, and challenges related to creating artificial datasets for LLM training and evaluation. The primary benefit is a comprehensive, up-to-date overview of the rapidly evolving landscape of LLM synthetic data.

How It Works

The repository functions as a living document, aggregating links to academic papers, blog posts, surveys, tools, and datasets. It categorizes these resources by method (e.g., pre-training, instruction tuning, model collapse, evaluation) and application area (e.g., mathematical reasoning, code generation, alignment, vision-language). This structured approach allows users to quickly navigate and discover relevant research and practical resources.

Quick Start & Requirements

This is a curated list of resources, not a software package. No installation or execution is required. Users can directly browse the README for links to papers, code repositories, and datasets.

Highlighted Details

• Extensive coverage of methods, including pre-training, instruction tuning, model collapse, and evaluation techniques.
• Detailed breakdown of application areas, such as mathematical reasoning, code generation, text-to-SQL, and alignment.
• Includes links to relevant tools and datasets for synthetic data generation.
• Regularly updated with recent research and developments in the field.

Maintenance & Community

The repository is actively maintained, with recent commits and a welcome message for Pull Requests. It is inspired by and links to the "Awesome-LLM-Synthetic-Data" repository.

Licensing & Compatibility

The repository is licensed under the MIT License, allowing for broad use and compatibility.

Limitations & Caveats

As a reading list, the repository's value is dependent on the quality and comprehensiveness of the linked external resources. It does not provide direct tools or code for synthetic data generation itself.