Capture the Flag: Uncovering Data Insights with Large Language Models
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Understanding Software Engineering Agents Through the Lens of Traceability: An Empirical Study
Graph-Structured Data Analysis of Component Failure in Autonomous Cargo Ships Based on Feature Fusion
The Devil behind the mask: An emergent safety vulnerability of Diffusion LLMs
Guiding LLM Decision-Making with Fairness Reward Models
Kodezi Chronos: A Debugging-First Language Model for Repository-Scale, Memory-Driven Code Understanding
InvestAlign: Overcoming Data Scarcity in Aligning Large Language Models with Investor Decision-Making Processes under Herd Behavior
토론
이 논문과 연결된 포럼 글이 아직 없습니다.