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.All open-source assets are publicly available at https://github.com/Lizruletheworld/Low-Confidence_Gold.
@article{arxiv.2502.18978,
title = {Low-Confidence Gold: Refining Low-Confidence Samples for Efficient Instruction Tuning},
author = {Hongyi Cai and Jie Li and Mohammad Mahdinur Rahman and Wenzhen Dong},
journal= {arXiv preprint arXiv:2502.18978},
year = {2026}
}