RICo: 面向自动指令微调数据选择的精细化上下文贡献度量
RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection
AI总结：
本研究提出无梯度的RICo方法量化指令微调样本的细粒度贡献，搭配轻量线性复杂度选择范式实现可扩展数据选择，实验证明其用少量数据即可超越全量训练及现有主流选择方法的性能。
摘要：
指令微调的数据选择对于提升大语言模型（LLM）性能、同时降低训练成本至关重要。本文提出了基于上下文学习的精细化贡献度量方法（Refined Contribution Measurement with In-Context Learning，简称RICo），这是一种全新的无梯度方法，可量化单个样本对任务级与全局模型性能的细粒度贡献。RICo能够更精准地识别高贡献数据，从而实现更优的指令微调效果。我们进一步引入了一种基于RICo分数训练的轻量级选择范式，可实现可扩展的数据选择，且推理复杂度严格为线性。我们在3个大语言模型、12个基准测试集和5组成对评估集上开展了大量实验，验证了RICo的有效性。值得注意的是，在LLaMA3.1-8B模型上，使用RICo选出的15%数据训练的模型，性能比使用全量数据集训练的模型高出5.42个百分点，且比常用选择方法的最佳性能高出2.06个百分点。我们进一步分析了RICo选出的高贡献样本，发现这些样本兼具任务多样性与难度适配性，并非仅包含难度最高的样本。
英文摘要：
Data selection for instruction tuning is crucial for improving the performance of large language models (LLMs) while reducing training costs. In this paper, we propose Refined Contribution Measurement with In-Context Learning (RICo), a novel gradient-free method that quantifies the fine-grained contribution of individual samples to both task-level and global-level model performance. RICo enables more accurate identification of high-contribution data, leading to better instruction tuning. We further introduce a lightweight selection paradigm trained on RICo scores, enabling scalable data selection with a strictly linear inference complexity. Extensive experiments on three LLMs across 12 benchmarks and 5 pairwise evaluation sets demonstrate the effectiveness of RICo. Remarkably, on LLaMA3.1-8B, models trained on 15% of RICo-selected data outperform full datasets by 5.42% points and exceed the best performance of widely used selection methods by 2.06% points. We further analyze high-contribution samples selected by RICo, which show both diverse tasks and appropriate difficulty levels, rather than just the hardest ones.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
