Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Yihan Cao, Yanbin Kang, Chi Wang, Lichao Sun

Research Area: Alignment, Data

Keywords: data-centric machine learning, large language models, generative models, language model finetuning

TL;DR: We present InstructMining, a method for selecting high-quality instruction-following data for finetuning large language models.

Abstract: Large language models (LLMs) are initially pretrained for broad capabilities and then finetuned with instruction-following datasets to improve their performance in interacting with humans. Despite advances in finetuning, a standardized guideline for selecting high-quality datasets to optimize this process remains elusive. In this paper, we first propose InstructMining, an innovative method designed for automatically selecting premium instruction-following data for finetuning LLMs. Specifically, InstructMining utilizes natural language indicators as a measure of data quality, applying them to evaluate unseen datasets. During experimentation, we discover that double descent phenomenon exists in large language model finetuning. Based on this observation, we further leverage BlendSearch to help find the best subset among the entire datase. Experiment results show that InstructMining-7B achieves state-of-the-art performance on two of the most popular benchmarks: LLM-as-a-judge and OpenLLM benchmark.

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Submission Number: 523