This repository is a curated list of papers and datasets focused on instruction tuning and following in large language models. It serves as a comprehensive resource for researchers and practitioners looking to understand and implement instruction-based learning, offering a structured overview of the field's advancements.

How It Works

The repository organizes research by categorizing papers into surveys, corpora, taxonomies (entailment-oriented, PLM-oriented, human-oriented), analyses (scale, interpretability, robustness, evaluation, negation, complexity), applications (HCI, data augmentation, general-purpose LLMs), and extended reading topics. It also provides a detailed table of instruction tuning datasets, including their release date, scale, annotation method, and number of tasks/instructions.

Quick Start & Requirements

This is a curated list, not a software package. No installation or execution is required. The primary resource is the collection of links to papers and datasets.

Highlighted Details

• Comprehensive taxonomy categorizing instruction types (entailment, PLM, human-oriented).
• Detailed table of instruction tuning datasets with key metadata.
• Extensive categorization of research papers by topic, including analyses and applications.
• Links to official paper PDFs, code repositories, and related resources.

Maintenance & Community

This repository is maintained by Renze Lou and Kai Zhang. Contributions are welcomed via pull requests or direct reach-out.

Licensing & Compatibility

The repository itself is not licensed as a software package. Individual papers and datasets are subject to their respective licenses.

Limitations & Caveats

As a curated list, the content's accuracy and completeness depend on ongoing community contributions and the maintainers' efforts. The rapidly evolving nature of LLM research means the list may not always be perfectly up-to-date.