@inproceedings{zhao-etal-2025-beyond-similarity,
title = "Beyond Similarity: A Gradient-based Graph Method for Instruction Tuning Data Selection",
author = "Zhao, Yang and
Du, Li and
Ding, Xiao and
Ouyang, Yangou and
Wang, Hepeng and
Xiong, Kai and
Gao, Jinglong and
Sun, Zhouhao and
Xu, Dongliang and
Yang, Qing and
Li, Dongchen and
Qin, Bing and
Liu, Ting",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1189/",
doi = "10.18653/v1/2025.acl-long.1189",
pages = "24391--24404",
ISBN = "979-8-89176-251-0",
abstract = "Large language models (LLMs) have shown great potential across various industries due to their remarkable ability to generalize through instruction tuning. However, the limited availability of domain-specific data significantly hampers their performance on specialized tasks. While existing methods primarily focus on selecting training data from general datasets that are similar to the target domain, they often fail to consider the joint distribution of instructions, resulting in inefficient learning and suboptimal knowledge transfer. To address these challenges, we introduce **G2IS** (**G**radient-based **G**raph **I**nstruction **S**election), a novel method that constructs a mixed gradient-based instruction graph to capture the joint distribution and interdependencies among instructions. By accounting for the relationships between instructions, G2IS improves domain adaptation efficiency. Additionally, we propose a gradient walk algorithm to refine the data selection process, enhancing both training effectiveness and efficiency. Our experiments demonstrate that G2IS outperforms traditional methods across various domain adaptation tasks, yielding significant performance gains, particularly in complex, data-scarce scenarios. These results underscore the potential of G2IS in advancing the development of large, domain-specific models."
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<abstract>Large language models (LLMs) have shown great potential across various industries due to their remarkable ability to generalize through instruction tuning. However, the limited availability of domain-specific data significantly hampers their performance on specialized tasks. While existing methods primarily focus on selecting training data from general datasets that are similar to the target domain, they often fail to consider the joint distribution of instructions, resulting in inefficient learning and suboptimal knowledge transfer. To address these challenges, we introduce **G2IS** (**G**radient-based **G**raph **I**nstruction **S**election), a novel method that constructs a mixed gradient-based instruction graph to capture the joint distribution and interdependencies among instructions. By accounting for the relationships between instructions, G2IS improves domain adaptation efficiency. Additionally, we propose a gradient walk algorithm to refine the data selection process, enhancing both training effectiveness and efficiency. Our experiments demonstrate that G2IS outperforms traditional methods across various domain adaptation tasks, yielding significant performance gains, particularly in complex, data-scarce scenarios. These results underscore the potential of G2IS in advancing the development of large, domain-specific models.</abstract>
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%0 Conference Proceedings
%T Beyond Similarity: A Gradient-based Graph Method for Instruction Tuning Data Selection
%A Zhao, Yang
%A Du, Li
%A Ding, Xiao
%A Ouyang, Yangou
%A Wang, Hepeng
%A Xiong, Kai
%A Gao, Jinglong
%A Sun, Zhouhao
%A Xu, Dongliang
%A Yang, Qing
%A Li, Dongchen
%A Qin, Bing
%A Liu, Ting
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F zhao-etal-2025-beyond-similarity
%X Large language models (LLMs) have shown great potential across various industries due to their remarkable ability to generalize through instruction tuning. However, the limited availability of domain-specific data significantly hampers their performance on specialized tasks. While existing methods primarily focus on selecting training data from general datasets that are similar to the target domain, they often fail to consider the joint distribution of instructions, resulting in inefficient learning and suboptimal knowledge transfer. To address these challenges, we introduce **G2IS** (**G**radient-based **G**raph **I**nstruction **S**election), a novel method that constructs a mixed gradient-based instruction graph to capture the joint distribution and interdependencies among instructions. By accounting for the relationships between instructions, G2IS improves domain adaptation efficiency. Additionally, we propose a gradient walk algorithm to refine the data selection process, enhancing both training effectiveness and efficiency. Our experiments demonstrate that G2IS outperforms traditional methods across various domain adaptation tasks, yielding significant performance gains, particularly in complex, data-scarce scenarios. These results underscore the potential of G2IS in advancing the development of large, domain-specific models.
%R 10.18653/v1/2025.acl-long.1189
%U https://aclanthology.org/2025.acl-long.1189/
%U https://doi.org/10.18653/v1/2025.acl-long.1189
%P 24391-24404
Markdown (Informal)
[Beyond Similarity: A Gradient-based Graph Method for Instruction Tuning Data Selection](https://aclanthology.org/2025.acl-long.1189/) (Zhao et al., ACL 2025)
ACL
- Yang Zhao, Li Du, Xiao Ding, Yangou Ouyang, Hepeng Wang, Kai Xiong, Jinglong Gao, Zhouhao Sun, Dongliang Xu, Qing Yang, Dongchen Li, Bing Qin, and Ting Liu. 2025. Beyond Similarity: A Gradient-based Graph Method for Instruction Tuning Data Selection. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 24391–24404, Vienna, Austria. Association for Computational Linguistics.