  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.
