Instruction-Tuning-Papers

Overview

A curated reading list of instruction-tuning papers, maintained by the community on GitHub. It tracks the trend starting from foundational works such as Natural-Instructions (ACL 2022), FLAN (ICLR 2022), and T0 (ICLR 2022). The repository serves as a reference for researchers and practitioners following developments in instruction tuning.

Best for

Best for
Researchers and developers who need a curated overview of instruction-tuning literature

Use cases

• Identifying seminal instruction-tuning papers for literature reviews
• Tracking the evolution of LLM alignment techniques
• Quickly finding key publications in the instruction-tuning area

Notes

A curated reading list of instruction-tuning papers, maintained by the community on GitHub. It tracks the trend starting from foundational works such as Natural-Instructions (ACL 2022), FLAN (ICLR 2022), and T0 (ICLR 2022). The repository serves as a reference for researchers and practitioners following developments in instruction tuning.

769 stars on GitHub. Last updated 2023-07-20.

Use cases

• Identifying seminal instruction-tuning papers for literature reviews
• Tracking the evolution of LLM alignment techniques
• Quickly finding key publications in the instruction-tuning area

Pros

• Covers influential papers from 2022 onward
• Community-maintained and openly accessible
• Provides a structured entry point for newcomers to instruction tuning

Cons

• Not a tool or framework, only a reading list
• May not include the most recent papers without manual updates
• Lacks detailed annotations, code, or comparison of methods

Indexed from awesome-llm and enriched against its public facts.