Data plays a fundamental role in training Large Language Models (LLMs). Efficient data management, particularly in formulating a well-suited training dataset, is significant for enhancing model performance and improving training efficiency during pretraining and supervised fine-tuning stages. Despite the considerable importance of data management, the underlying mechanism of current prominent practices are still unknown. Consequently, the exploration of data management has attracted more and more attention among the research community. This survey aims to provide a comprehensive overview of current research in data management within both the pretraining and supervised fine-tuning stages of LLMs, covering various aspects of data management strategy design. Looking into the future, we extrapolate existing challenges and outline promising directions for development in this field. Therefore, this survey serves as a guiding resource for practitioners aspiring to construct powerful LLMs through efficient data management practices. The collection of the latest papers is available at https://github.com/ZigeW/data_management_LLM.
@article{arxiv.2312.01700,
title = {Data Management For Training Large Language Models: A Survey},
author = {Zige Wang and Wanjun Zhong and Yufei Wang and Qi Zhu and Fei Mi and Baojun Wang and Lifeng Shang and Xin Jiang and Qun Liu},
journal= {arXiv preprint arXiv:2312.01700},
year = {2024}
}