MergeIT: From Selection to Merging for Efficient Instruction Tuning
This work addresses efficiency and diversity issues in instruction tuning for LLM developers, though it appears incremental as it builds on existing merging and filtering techniques.
The paper tackles the high computational cost and reduced data diversity in instruction tuning for Large Language Models by proposing MergeIT, a strategy that shifts from selection to synthesis, resulting in more informative and compact training data with improved efficiency and diversity.
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