This repository hosts the data accompanying the ACL 2025 main conference paper "Measuring Data Diversity for Instruction Tuning: A Systematic Analysis and A Reliable Metric".
In this research, we tackle the fundamental challenge of accurately measuring dataset diversity for instruction tuning and introduce NovelSum, a reliable diversity metric that jointly accounts for inter-sample distances and information density, and shows a strong correlation with model performance.… See the full description on the dataset page:
https://huggingface.co/datasets/Sirius518/NovelSum.
