Capture the Flag: Uncovering Data Insights with Large Language Models

TL;DR

This paper investigates using Large Language Models to automate data insight discovery through a novel 'capture the flag' evaluation, demonstrating preliminary but promising results in real-world datasets.

Contribution

It introduces a new evaluation methodology for LLMs in data insight extraction and compares two proof-of-concept agents on real-world data.

Findings

Preliminary results show potential for LLMs in data insight tasks.

The 'capture the flag' methodology effectively measures relevant information recognition.

Different agent architectures exhibit varying success in flag detection.

Abstract

The extraction of a small number of relevant insights from vast amounts of data is a crucial component of data-driven decision-making. However, accomplishing this task requires considerable technical skills, domain expertise, and human labor. This study explores the potential of using Large Language Models (LLMs) to automate the discovery of insights in data, leveraging recent advances in reasoning and code generation techniques. We propose a new evaluation methodology based on a "capture the flag" principle, measuring the ability of such models to recognize meaningful and pertinent information (flags) in a dataset. We further propose two proof-of-concept agents, with different inner workings, and compare their ability to capture such flags in a real-world sales dataset. While the work reported here is preliminary, our results are sufficiently interesting to mandate future exploration by the community.