Stratified Selective Sampling for Instruction Tuning with Dedicated Scoring Strategy

ACL ARR 2025 May Submission5086 Authors

Abstract: Recent work shows that post-training datasets for LLMs can be substantially downsampled without noticeably deteriorating performance. However, data selection often incurs high computational costs or is limited to narrow domains. In this paper, we demonstrate that data selection can be both---efficient and universal---by using a multi-step pipeline in which we efficiently bin data points into groups, estimate quality using specialized models, and score difficulty with a robust, lightweight method. Task-based categorization allows us to control the composition of our final data---crucial for finetuning multi-purpose models. To guarantee diversity, we improve upon previous work using embedding models and a clustering algorithm. This integrated strategy enables high-performance fine-tuning with minimal overhead.

Paper Type: Long

Research Area: Resources and Evaluation

Research Area Keywords: instruction tuning,data selection,dedicated scoring strategy,stratified sampling,instruction classification

Contribution Types: NLP engineering experiment, Approaches low compute settings-efficiency, Data analysis

Languages Studied: English

Submission Number: 5086