Low-Confidence Gold: Refining Low-Confidence Samples for Efficient Instruction Tuning

ACL ARR 2025 May Submission398 Authors

Abstract: The effectiveness of instruction fine-tuning for Large Language Models is fundamentally constrained by the quality and efficiency of training datasets. This work introduces Low-Confidence Gold (LCG), a novel filtering framework that employs centroid-based clustering and confidence-guided selection for identifying valuable instruction pairs. Through a semi-supervised approach using a lightweight classifier trained on representative samples, LCG curates high-quality subsets while preserving data diversity. Experimental evaluation demonstrates that models fine-tuned on LCG-filtered subsets of 6K samples achieve superior performance compared to existing methods, with substantial improvements on MT-bench and consistent gains across comprehensive evaluation metrics. The framework's efficacy while maintaining model performance establishes a promising direction for efficient instruction tuning.

Paper Type: Short

Research Area: Machine Translation

Research Area Keywords: Instruction Finetuning, Data Selection, Semi-supervised Learning

Contribution Types: NLP engineering experiment, Approaches to low-resource settings, Data resources

Languages Studied: English

Submission Number: 398