@inproceedings{cai-etal-2026-mergeit,
title = "{M}erge{IT}: From Selection to Merging for Efficient Instruction Tuning",
author = "Cai, Hongyi and
Fu, Yuqian and
Fu, Hongming and
Zhao, Bo",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.46/",
pages = "912--923",
ISBN = "979-8-89176-395-1",
abstract = "Instruction tuning is crucial for optimizing Large Language Models (LLMs), as the quality and diversity of instructional data significantly influence model performance. This naturally underscores the importance of an effective and efficient data selection strategy. However, recent mainstream data selection methods typically rely on LLMs to score instruction quality{---}taking advantage of their capabilities, but at the cost of high computational overhead and reduced data diversity. To address these limitations, in this paper, we propose MergeIT, a novel LLM-based Merging strategy for better Instruction Tuning that shifts the focus from selection to synthesis. MergeIT consists of two stages: first, topic-aware filtering clusters and refines the dataset, preserving diversity while eliminating redundancy without relying on LLM-based scoring, significantly reducing time and computational cost. Second, LLM-based merging synthesizes semantically similar instructions into more informative and compact training data, enhancing data richness while further reducing the size of the dataset. Experimental results demonstrate that MergeIT enables efficient, diverse, and scalable instruction selection and synthesis, establishing LLM-based merging as a promising alternative to prior scoring-based selection methods for instruction tuning."
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<abstract>Instruction tuning is crucial for optimizing Large Language Models (LLMs), as the quality and diversity of instructional data significantly influence model performance. This naturally underscores the importance of an effective and efficient data selection strategy. However, recent mainstream data selection methods typically rely on LLMs to score instruction quality—taking advantage of their capabilities, but at the cost of high computational overhead and reduced data diversity. To address these limitations, in this paper, we propose MergeIT, a novel LLM-based Merging strategy for better Instruction Tuning that shifts the focus from selection to synthesis. MergeIT consists of two stages: first, topic-aware filtering clusters and refines the dataset, preserving diversity while eliminating redundancy without relying on LLM-based scoring, significantly reducing time and computational cost. Second, LLM-based merging synthesizes semantically similar instructions into more informative and compact training data, enhancing data richness while further reducing the size of the dataset. Experimental results demonstrate that MergeIT enables efficient, diverse, and scalable instruction selection and synthesis, establishing LLM-based merging as a promising alternative to prior scoring-based selection methods for instruction tuning.</abstract>
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%0 Conference Proceedings
%T MergeIT: From Selection to Merging for Efficient Instruction Tuning
%A Cai, Hongyi
%A Fu, Yuqian
%A Fu, Hongming
%A Zhao, Bo
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F cai-etal-2026-mergeit
%X Instruction tuning is crucial for optimizing Large Language Models (LLMs), as the quality and diversity of instructional data significantly influence model performance. This naturally underscores the importance of an effective and efficient data selection strategy. However, recent mainstream data selection methods typically rely on LLMs to score instruction quality—taking advantage of their capabilities, but at the cost of high computational overhead and reduced data diversity. To address these limitations, in this paper, we propose MergeIT, a novel LLM-based Merging strategy for better Instruction Tuning that shifts the focus from selection to synthesis. MergeIT consists of two stages: first, topic-aware filtering clusters and refines the dataset, preserving diversity while eliminating redundancy without relying on LLM-based scoring, significantly reducing time and computational cost. Second, LLM-based merging synthesizes semantically similar instructions into more informative and compact training data, enhancing data richness while further reducing the size of the dataset. Experimental results demonstrate that MergeIT enables efficient, diverse, and scalable instruction selection and synthesis, establishing LLM-based merging as a promising alternative to prior scoring-based selection methods for instruction tuning.
%U https://aclanthology.org/2026.findings-acl.46/
%P 912-923
Markdown (Informal)
[MergeIT: From Selection to Merging for Efficient Instruction Tuning](https://aclanthology.org/2026.findings-acl.46/) (Cai et al., Findings 2026)
ACL