用上下文影响度筛选指令数据，提升小样本下的模型表现
What Makes Good Instruction-Tuning Data? An In-Context Learning Perspective
- 基于上下文影响度加权筛选候选样本
- 样本越难，其上下文影响力越低
- 在有限数据下显著优于现有方法，适合数据受限场景
指令微调数据集常存在大量冗余和低质量样本，需高效的数据选择方法。本文提出基于加权上下文影响（wICI）的指令数据筛选框架，衡量每个候选样本对语义相关同伴减少指令遵循难度的有效性。通过多模型、多基准的系统实验，回答三个核心问题：从上下文视角看，什么构成有效指令微调数据；样本难度是否与上下文影响相关；上下文影响如何转化为微调效果。实验表明，在数据预算受限条件下，该方法持续优于现有基线，且样本难度与上下文影响呈负相关。
原文摘要 · Abstract (English)
Instruction-tuning datasets often contain substantial redundancy and low-quality samples, necessitating effective data selection methods. We propose an instruction data selection framework based on weighted in-context influence (wICI), which measures how effectively each candidate example reduces instruction-following difficulty for semantically related peers. Through systematic experiments, we address three key questions: what constitutes effective instruction tuning data from an in-context perspective, whether sample difficulty correlates with in-context influence, and how in-context influence translates to instruction tuning effectiveness. Experiments across multiple models and benchmarks demonstrate that our method consistently outperforms existing baselines under constrained data budgets, while empirically showing that sample difficulty negatively correlates with in-context influence.
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