@inproceedings{li-etal-2025-mosaic,
title = "Mosaic-{IT}: Cost-Free Compositional Data Synthesis for Instruction Tuning",
author = "Li, Ming and
Chen, Pei and
Wang, Chenguang and
Zhao, Hongyu and
Liang, Yijun and
Hou, YuPeng and
Liu, Fuxiao and
Zhou, Tianyi",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.1297/",
doi = "10.18653/v1/2025.findings-acl.1297",
pages = "25287--25318",
ISBN = "979-8-89176-256-5",
abstract = "Finetuning large language models with a variety of instruction-response pairs has enhanced their capability to understand and follow instructions. Current instruction tuning primarily relies on teacher models or human intervention to generate and refine the instructions and responses for training, which are costly, non-sustainable, and may lack diversity. In this paper, we introduce Mosaic Instruction Tuning (Mosaic-IT), a human/model-free compositional data synthesis method that can efficiently create rich and diverse augmentations from existing instruction tuning data to enhance the LLMs. Mosaic-IT randomly concatenates multiple instruction data into one and trains the model to produce the corresponding responses with predefined higher-level meta-instructions to strengthen its multi-step instruction-following and format-following skills. Our extensive evaluations demonstrate a superior performance and training efficiency of Mosaic-IT, which achieves consistent performance improvements over various benchmarks and an 80{\%} reduction in training costs compared with original instruction tuning."
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<abstract>Finetuning large language models with a variety of instruction-response pairs has enhanced their capability to understand and follow instructions. Current instruction tuning primarily relies on teacher models or human intervention to generate and refine the instructions and responses for training, which are costly, non-sustainable, and may lack diversity. In this paper, we introduce Mosaic Instruction Tuning (Mosaic-IT), a human/model-free compositional data synthesis method that can efficiently create rich and diverse augmentations from existing instruction tuning data to enhance the LLMs. Mosaic-IT randomly concatenates multiple instruction data into one and trains the model to produce the corresponding responses with predefined higher-level meta-instructions to strengthen its multi-step instruction-following and format-following skills. Our extensive evaluations demonstrate a superior performance and training efficiency of Mosaic-IT, which achieves consistent performance improvements over various benchmarks and an 80% reduction in training costs compared with original instruction tuning.</abstract>
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%0 Conference Proceedings
%T Mosaic-IT: Cost-Free Compositional Data Synthesis for Instruction Tuning
%A Li, Ming
%A Chen, Pei
%A Wang, Chenguang
%A Zhao, Hongyu
%A Liang, Yijun
%A Hou, YuPeng
%A Liu, Fuxiao
%A Zhou, Tianyi
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F li-etal-2025-mosaic
%X Finetuning large language models with a variety of instruction-response pairs has enhanced their capability to understand and follow instructions. Current instruction tuning primarily relies on teacher models or human intervention to generate and refine the instructions and responses for training, which are costly, non-sustainable, and may lack diversity. In this paper, we introduce Mosaic Instruction Tuning (Mosaic-IT), a human/model-free compositional data synthesis method that can efficiently create rich and diverse augmentations from existing instruction tuning data to enhance the LLMs. Mosaic-IT randomly concatenates multiple instruction data into one and trains the model to produce the corresponding responses with predefined higher-level meta-instructions to strengthen its multi-step instruction-following and format-following skills. Our extensive evaluations demonstrate a superior performance and training efficiency of Mosaic-IT, which achieves consistent performance improvements over various benchmarks and an 80% reduction in training costs compared with original instruction tuning.
%R 10.18653/v1/2025.findings-acl.1297
%U https://aclanthology.org/2025.findings-acl.1297/
%U https://doi.org/10.18653/v1/2025.findings-acl.1297
%P 25287-25318
Markdown (Informal)
[Mosaic-IT: Cost-Free Compositional Data Synthesis for Instruction Tuning](https://aclanthology.org/2025.findings-acl.1297/) (Li et al., Findings 2025)
ACL
- Ming Li, Pei Chen, Chenguang Wang, Hongyu Zhao, Yijun Liang, YuPeng Hou, Fuxiao Liu, and Tianyi Zhou. 2025. Mosaic-IT: Cost-Free Compositional Data Synthesis for Instruction Tuning. In Findings of the Association for Computational Linguistics: ACL 2025, pages 25287–25318, Vienna, Austria. Association for Computational Linguistics.