arXiv Computation and Language October 1, 2026 By Hao Liang, Zhengyang Zhao, Mingrui Chen, Meiyi Qiang, Lu Ma, Rongyi Yu, Hengyi Feng, Shixuan Sun, Zimo Meng, Xiaochen Ma, Xuanlin Yang, Qifeng Cai, Ruichuan An, Bohan Zeng, Zhen Hao Wong, Chengyu Shen, Runming He, Zhaoyang Han, Yaowei Zheng, Fangcheng Fu, Conghui He, Bin Cui, Zhiyu Li, Weinan E, Wentao Zhang

DataFlex: A Unified Framework for Data-Centric Dynamic Training of Large Language Models

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arXiv AI

Jul 28

DomainPilot: Domain-Level Loss-Guided Two-Stage Data Mixture Optimization for Efficient Language Model Fine-Tuning

arXiv:2607. 22769v1 Announce Type: cross Abstract: The training efficacy of large language models (LLMs) is fundamentally constrained by the quality and composition of training data.

By He Zhang

llms fine-tuning benchmarks data-engineering

arXiv Machine Learning

Jun 2

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining

arXiv:2510. 06048v4 Announce Type: replace Abstract: Effective data selection is essential for pretraining large language models (LLMs), enhancing efficiency and improving generalization to downstream tasks.

By Jie Hao, Rui Yu, Wei Zhang, Huixia Wang, Jie Xu, Mingrui Liu

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arXiv Machine Learning

Jul 24

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators

arXiv:2607. 20465v1 Announce Type: new Abstract: The quality of training data fundamentally determines the capabilities of large language models (LLMs), yet no unified benchmark exists to measure how well LLMs, agents, and data-centric workflows actually prepare training data end to end.

By Hao Liang, Qifeng Cai, Yibo Lin, Jianzhuo Du, Qifeng Xia, Sizhe Qiu, Linzhuang Sun, Meiyi Qiang, Zhaoyang Han, Xiaochen Ma, Bohan Zeng, Ruichuan An, Conghui He, Wentao Zhang

llms agents fine-tuning benchmarks safety data-engineering

arXiv AI

Jul 2

CausalMix: Data Mixture as Causal Inference for Language Model Training

arXiv:2607. 01104v1 Announce Type: cross Abstract: In Large Language Model (LLM) training, data mixing plays a pivotal role in determining model performance.

By Zinan Tang, Yukun Zhang, Shaomian Zheng, Zhuoshi Pan, Qizhi Pei, Dingnan Jin, Jun Zhou, Yujun Wang, Biqing Huang

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arXiv AI

Jul 29

REPREC: Representation Driven Parameter-Efficient Recommendation System

arXiv:2607. 24845v1 Announce Type: cross Abstract: Large language models (LLMs) have been applied to sequential recommendation by formulating it as a natural language task.

By Harshini Kavuru, Dwipam Katariya, Giri Iyengar, Pranab Mohanty, Kalanand Mishra, Kalanand Mishra

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arXiv Computation and Language

Aug 21

EvoSelect: Data-Efficient LLM Evolution for Targeted Task Adaptation

arXiv:2604. 26170v2 Announce Type: replace Abstract: Adapting large language models (LLMs) to a targeted task efficiently and effectively remains a fundamental challenge.

By Ting-Wei Li, Sirui Chen, Jiaru Zou, Yingbing Huang, Tianxin Wei, Jingrui He, Hanghang Tong

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Related stories

DomainPilot: Domain-Level Loss-Guided Two-Stage Data Mixture Optimization for Efficient Language Model Fine-Tuning

BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators

CausalMix: Data Mixture as Causal Inference for Language Model Training

REPREC: Representation Driven Parameter-Efficient Recommendation System

EvoSelect: Data-Efficient LLM Evolution for Targeted Task Adaptation