MergeIT：从选择到合并的高效指令微调
MergeIT: From Selection to Merging for Efficient Instruction Tuning
AI总结：
针对指令微调中数据选择方法计算成本高且降低多样性的问题，提出两阶段策略MergeIT，通过主题感知过滤和基于LLM的指令合并，在不依赖评分的情况下合成紧凑且丰富的训练数据，实现高效可扩展的指令微调。
摘要：
指令微调对于优化大型语言模型（LLMs）至关重要，然而主流的数据选择方法严重依赖LLMs作为指令质量评分器，导致计算成本高昂且数据多样性降低。为了解决这些局限性，我们提出了MergeIT，一种基于LLM的合并策略，旨在实现更好的指令微调，将重点从选择转向合成。MergeIT分为两个阶段运行：首先，主题感知过滤对数据集进行聚类和精炼，在不依赖基于LLM评分的情况下保持多样性并消除冗余。其次，基于LLM的合并将语义相似的指令合成为更具信息量和更紧凑的训练数据，在进一步减少数据集规模的同时增强数据丰富度。实验结果表明，MergeIT能够实现高效、多样和可扩展的指令选择与合成，确立了基于LLM的合并作为传统基于评分的指令微调选择方法的一种有前景的替代方案。我们的源代码和数据集现已发布于 https://github.com/XcloudFance/MergeIT
英文摘要：
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
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。
