用合成代替筛选，让大模型更高效地生成高质量指令数据。
MergeIT: From Selection to Merging for Efficient Instruction Tuning
- 先按主题过滤并精炼数据，不依赖大模型评分，保留多样性。
- 通过大模型合并语义相似指令，数据量减少30%仍更丰富。
- 适合追求高效训练且重视数据多样性的LLM研究者。
指令微调对优化大语言模型至关重要，但主流数据筛选方法过度依赖大模型作为指令质量评分器，导致计算成本高且数据多样性降低。为解决这些问题，我们提出MergeIT，一种基于大模型的新型合并策略，将焦点从筛选转向合成。MergeIT分两阶段运行：第一阶段，基于主题感知的过滤与聚类，精炼数据集，在不依赖大模型评分的前提下保留多样性并消除冗余；第二阶段，利用大模型合并语义相近的指令，生成信息更丰富、体积更小的训练数据，进一步提升数据密度并压缩规模。实验表明，MergeIT实现了高效、多样且可扩展的指令筛选与合成，证明了大模型合并是传统评分式筛选的有力替代方案。代码与数据集已开源于 https://github.com/XcloudFance/MergeIT。
原文摘要 · Abstract (English)
Instruction tuning is crucial for optimizing Large Language Models (LLMs), yet mainstream data selection methods heavily rely on LLMs as instruction quality scorers, leading to high computational costs and reduced data diversity. To address these limitations, we propose MergeIT, a novel LLM-based Merging strategy for better Instruction Tuning that shifts the focus from selection to synthesis. MergeIT operates in two stages: first, topic-aware filtering clusters and refines the dataset, preserving diversity while eliminating redundancy without relying on LLM-based scoring. Second, LLM-based merging synthesizes semantically similar instructions into more informative and compact training data, enhancing data richness while further reducing dataset size. Experimental results demonstrate that MergeIT enables efficient, diverse, and scalable instruction selection and synthesis, establishing LLM-based merging as a promising alternative to conventional scoring-based selection methods for instruction tuning. Our source code and datasets are now available at https://github.com/XcloudFance/MergeIT
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