@inproceedings{xi-etal-2025-samplemix,
title = "{S}ample{M}ix: A Sample-wise Pre-training Data Mixing Strategy by Coordinating Data Quality and Diversity",
author = "Xi, Xiangyu and
Kong, Deyang and
Yang, Jian and
Yang, Jiawei and
Chen, Zhengyu and
Wang, Wei and
Wang, Jingang and
Cai, Xunliang and
Zhang, Shikun and
Ye, Wei",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.741/",
pages = "13736--13758",
ISBN = "979-8-89176-335-7",
abstract = "Existing pretraining data mixing methods for large language models (LLMs) typically follow a domain-wise methodology, a top-down process that first determines domain weights and then performs uniform data sampling across each domain. However, these approaches neglect significant inter-domain overlaps and commonalities, failing to control the global diversity of the constructed training dataset. Further, uniform sampling within domains ignores fine-grained sample-specific features, potentially leading to suboptimal data distribution. To address these shortcomings, we propose a novel sample-wise data mixture approach based on a bottom-up paradigm. This method performs global cross-domain sampling by systematically evaluating the quality and diversity of each sample, thereby dynamically determining the optimal domain distribution. Comprehensive experiments across multiple downstream tasks and perplexity assessments demonstrate that SampleMix surpasses existing domain-based methods. Meanwhile, SampleMix requires 1.4x to 2.1x fewer training steps to achieve the baselines' performance, highlighting the substantial potential of SampleMix to optimize pre-training data."
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<abstract>Existing pretraining data mixing methods for large language models (LLMs) typically follow a domain-wise methodology, a top-down process that first determines domain weights and then performs uniform data sampling across each domain. However, these approaches neglect significant inter-domain overlaps and commonalities, failing to control the global diversity of the constructed training dataset. Further, uniform sampling within domains ignores fine-grained sample-specific features, potentially leading to suboptimal data distribution. To address these shortcomings, we propose a novel sample-wise data mixture approach based on a bottom-up paradigm. This method performs global cross-domain sampling by systematically evaluating the quality and diversity of each sample, thereby dynamically determining the optimal domain distribution. Comprehensive experiments across multiple downstream tasks and perplexity assessments demonstrate that SampleMix surpasses existing domain-based methods. Meanwhile, SampleMix requires 1.4x to 2.1x fewer training steps to achieve the baselines’ performance, highlighting the substantial potential of SampleMix to optimize pre-training data.</abstract>
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%0 Conference Proceedings
%T SampleMix: A Sample-wise Pre-training Data Mixing Strategy by Coordinating Data Quality and Diversity
%A Xi, Xiangyu
%A Kong, Deyang
%A Yang, Jian
%A Yang, Jiawei
%A Chen, Zhengyu
%A Wang, Wei
%A Wang, Jingang
%A Cai, Xunliang
%A Zhang, Shikun
%A Ye, Wei
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F xi-etal-2025-samplemix
%X Existing pretraining data mixing methods for large language models (LLMs) typically follow a domain-wise methodology, a top-down process that first determines domain weights and then performs uniform data sampling across each domain. However, these approaches neglect significant inter-domain overlaps and commonalities, failing to control the global diversity of the constructed training dataset. Further, uniform sampling within domains ignores fine-grained sample-specific features, potentially leading to suboptimal data distribution. To address these shortcomings, we propose a novel sample-wise data mixture approach based on a bottom-up paradigm. This method performs global cross-domain sampling by systematically evaluating the quality and diversity of each sample, thereby dynamically determining the optimal domain distribution. Comprehensive experiments across multiple downstream tasks and perplexity assessments demonstrate that SampleMix surpasses existing domain-based methods. Meanwhile, SampleMix requires 1.4x to 2.1x fewer training steps to achieve the baselines’ performance, highlighting the substantial potential of SampleMix to optimize pre-training data.
%U https://aclanthology.org/2025.findings-emnlp.741/
%P 13736-13758
Markdown (Informal)
[SampleMix: A Sample-wise Pre-training Data Mixing Strategy by Coordinating Data Quality and Diversity](https://aclanthology.org/2025.findings-emnlp.741/) (Xi et al., Findings 2025)
ACL
- Xiangyu Xi, Deyang Kong, Jian Yang, Jiawei Yang, Zhengyu Chen, Wei Wang, Jingang Wang, Xunliang Cai, Shikun Zhang, and Wei Ye. 2025. SampleMix: A Sample-wise Pre-training Data Mixing Strategy by Coordinating Data Quality and Diversity. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 13736–13758, Suzhou, China. Association for Computational Linguistics.