@inproceedings{shi-etal-2025-dids,
title = "{DIDS}: Domain Impact-aware Data Sampling for Large Language Model Training",
author = "Shi, Weijie and
Zhang, Jipeng and
Wu, Yaguang and
Fang, Jingzhi and
Zhang, Shibo and
Zhao, Yao and
Chen, Hao and
Zhang, Ruiyuan and
Cui, Yue and
Zhu, Jia and
Han, Sirui and
Xu, Jiajie and
Zhou, Xiaofang",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.215/",
pages = "4330--4350",
ISBN = "979-8-89176-332-6",
abstract = "Large language models (LLMs) are commonly trained on multi-domain datasets, where domain sampling strategies significantly impact model performance due to varying domain importance across downstream tasks. Existing approaches for optimizing domain-level sampling strategies struggle with maintaining intra-domain consistency and accurately measuring domain impact. In this paper, we present Domain Impact-aware Data Sampling (DIDS). To ensure intra-domain consistency, a gradient clustering algorithm is proposed to group training data based on their learning effects, where a proxy language model and dimensionality reduction are employed to reduce computational overhead. To accurately measure domain impact, we develop a Fisher Information Matrix (FIM) guided metric that quantifies how domain-specific parameter updates affect the model{'}s output distributions on downstream tasks, with theoretical guarantees. Furthermore, to determine optimal sampling ratios, DIDS combines both the FIM-guided domain impact assessment and loss learning trajectories that indicate domain-specific potential, while accounting for diminishing marginal returns. Extensive experiments demonstrate that DIDS achieves 3.4{\%} higher average performance while maintaining comparable training efficiency. The code is available at https://github.com/shiweijiezero/DIDS."
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<abstract>Large language models (LLMs) are commonly trained on multi-domain datasets, where domain sampling strategies significantly impact model performance due to varying domain importance across downstream tasks. Existing approaches for optimizing domain-level sampling strategies struggle with maintaining intra-domain consistency and accurately measuring domain impact. In this paper, we present Domain Impact-aware Data Sampling (DIDS). To ensure intra-domain consistency, a gradient clustering algorithm is proposed to group training data based on their learning effects, where a proxy language model and dimensionality reduction are employed to reduce computational overhead. To accurately measure domain impact, we develop a Fisher Information Matrix (FIM) guided metric that quantifies how domain-specific parameter updates affect the model’s output distributions on downstream tasks, with theoretical guarantees. Furthermore, to determine optimal sampling ratios, DIDS combines both the FIM-guided domain impact assessment and loss learning trajectories that indicate domain-specific potential, while accounting for diminishing marginal returns. Extensive experiments demonstrate that DIDS achieves 3.4% higher average performance while maintaining comparable training efficiency. The code is available at https://github.com/shiweijiezero/DIDS.</abstract>
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%0 Conference Proceedings
%T DIDS: Domain Impact-aware Data Sampling for Large Language Model Training
%A Shi, Weijie
%A Zhang, Jipeng
%A Wu, Yaguang
%A Fang, Jingzhi
%A Zhang, Shibo
%A Zhao, Yao
%A Chen, Hao
%A Zhang, Ruiyuan
%A Cui, Yue
%A Zhu, Jia
%A Han, Sirui
%A Xu, Jiajie
%A Zhou, Xiaofang
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F shi-etal-2025-dids
%X Large language models (LLMs) are commonly trained on multi-domain datasets, where domain sampling strategies significantly impact model performance due to varying domain importance across downstream tasks. Existing approaches for optimizing domain-level sampling strategies struggle with maintaining intra-domain consistency and accurately measuring domain impact. In this paper, we present Domain Impact-aware Data Sampling (DIDS). To ensure intra-domain consistency, a gradient clustering algorithm is proposed to group training data based on their learning effects, where a proxy language model and dimensionality reduction are employed to reduce computational overhead. To accurately measure domain impact, we develop a Fisher Information Matrix (FIM) guided metric that quantifies how domain-specific parameter updates affect the model’s output distributions on downstream tasks, with theoretical guarantees. Furthermore, to determine optimal sampling ratios, DIDS combines both the FIM-guided domain impact assessment and loss learning trajectories that indicate domain-specific potential, while accounting for diminishing marginal returns. Extensive experiments demonstrate that DIDS achieves 3.4% higher average performance while maintaining comparable training efficiency. The code is available at https://github.com/shiweijiezero/DIDS.
%U https://aclanthology.org/2025.emnlp-main.215/
%P 4330-4350
Markdown (Informal)
[DIDS: Domain Impact-aware Data Sampling for Large Language Model Training](https://aclanthology.org/2025.emnlp-main.215/) (Shi et al., EMNLP 2025)
ACL
- Weijie Shi, Jipeng Zhang, Yaguang Wu, Jingzhi Fang, Shibo Zhang, Yao Zhao, Hao Chen, Ruiyuan Zhang, Yue Cui, Jia Zhu, Sirui Han, Jiajie Xu, and Xiaofang Zhou. 2025. DIDS: Domain Impact-aware Data Sampling for Large Language Model Training. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 4330–4350, Suzhou, China. Association for Computational Linguistics.