RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection

• URL: http://arxiv.org/abs/2505.05327v2
• Date: Sun, 18 May 2025 11:24:09 GMT
• Title: RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection
• Authors: Yixin Yang, Qingxiu Dong, Linli Yao, Fangwei Zhu, Zhifang Sui,
• Abstract summary: We propose a gradient-free method that quantifies the fine-grained contribution of individual samples to both task-level and global-level model performance.<n>We introduce a lightweight selection paradigm trained on RICo scores, enabling scalable data selection with a strictly linear inference complexity.
• Score: 29.459431336830267
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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