Diversity Measurement and Subset Selection for Instruction Tuning Datasets
This work addresses the challenge of efficient dataset curation for fine-tuning large language models, offering a method to enhance instruction-following capabilities, though it is incremental as it builds on prior diversity-focused heuristics.
The paper tackled the problem of selecting diverse data subsets for instruction tuning of large language models by proposing a diversity measurement using determinantal point processes and log determinant distance, showing that this measure correlates with improved downstream instruction-following performance in experiments.
We aim to select data subsets for the fine-tuning of large language models to more effectively follow instructions. Prior work has emphasized the importance of diversity in dataset curation but relied on heuristics such as the number of tasks. In this paper, we use determinantal point processes to capture the diversity and quality of instruction tuning datasets for subset selection. We propose to measure dataset diversity with log determinant distance that is the distance between the dataset of interest and a maximally diverse reference dataset. Our experiments demonstrate that the proposed diversity measure in the normalized weight gradient space is correlated with downstream instruction-following performance. Consequently, it can be used to inform when data selection is the most helpful and to analyze dataset curation strategies. We demonstrate the utility of our approach on various instruction tuning datasets.