A Survey on Large Language Model-based Agents for Statistics and Data Science

TL;DR

This survey reviews the development, capabilities, and applications of Large Language Model-based data agents in statistics and data science, emphasizing their potential to simplify complex tasks and enhance user accessibility.

Contribution

It provides a comprehensive overview of LLM-based data agents, detailing their design features, practical applications, and future research challenges in statistical analysis.

Findings

LLM-based data agents facilitate complex data analysis with minimal human input

Case studies demonstrate successful real-world applications of data agents

Identifies key challenges and future directions for intelligent statistical software

Abstract

In recent years, data science agents powered by Large Language Models (LLMs), known as "data agents," have shown significant potential to transform the traditional data analysis paradigm. This survey provides an overview of the evolution, capabilities, and applications of LLM-based data agents, highlighting their role in simplifying complex data tasks and lowering the entry barrier for users without related expertise. We explore current trends in the design of LLM-based frameworks, detailing essential features such as planning, reasoning, reflection, multi-agent collaboration, user interface, knowledge integration, and system design, which enable agents to address data-centric problems with minimal human intervention. Furthermore, we analyze several case studies to demonstrate the practical applications of various data agents in real-world scenarios. Finally, we identify key challenges and propose future research directions to advance the development of data agents into intelligent statistical analysis software.