Data Management For Training Large Language Models: A Survey

TL;DR

This survey reviews current data management strategies for training large language models, emphasizing their importance in improving model performance and training efficiency, and discusses future challenges and directions.

Contribution

It provides a comprehensive overview of data management practices in LLM training and outlines future research challenges and promising directions.

Findings

Current practices are not fully understood mechanistically.

Efficient data management enhances LLM performance.

Future research should address identified challenges.

Abstract

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.