用低置信度样本筛选法，6000条数据就能高效提升指令微调效果
Low-Confidence Gold: Refining Low-Confidence Samples for Efficient Instruction Tuning
- 通过聚类和置信度筛选，自动找出高价值指令对
- 仅用6000条样本即超越现有方法，多指标领先
- 适合想省数据、提效果的模型训练者
大语言模型指令微调的效果受限于训练数据的质量与效率。本文提出低置信度黄金（LCG）筛选框架，结合基于质心的聚类与置信度引导选择，识别有价值的指令对。通过轻量级分类器在代表性样本上进行半监督训练，LCG在保留数据多样性的同时，构建高质量子集。实验表明，使用LCG筛选的6000样本子集进行微调，模型在MT-bench等基准上表现更优，各项评估指标均有显著提升。该框架在保持模型性能的前提下实现高效微调，为指令微调提供了新方向。所有开源资源已公开于https://github.com/Lizruletheworld/Low-Confidence_Gold。
原文摘要 · Abstract (English)
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.
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