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 proposeInstructMining, an innovative method designed for automatically selecting premium instruction-following data for finetuning LLMs. Specifically,InstructMiningutilizes 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 leverageBlendSearchto help find the best subset among the entire dataset (i.e., 2,532 out of 100,000) .
Experiment results show thatInstructMining-7B achieves state-of-the-art performance on two of the most popular benchmarks:LLM-as-a-judgeand HuggingfaceOpenLLM.