Capture the Flag: Uncovering Data Insights with Large Language Models

Issam H. Laradji, Perouz Taslakian, Sai Rajeswar, Valentina Zantedeschi, Alexandre Lacoste, Nicolas Chapados, David Vazquez, Christopher Pal, Alexandre Drouin

Keywords: llms, data analysis, copilot, capture the flag, insights, prompting

TL;DR: Proposing methods and a benchmark to uncover data insights with Large Language Models

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.

Submission Number: 95