LLM as Dataset Analyst: Subpopulation Structure Discovery with Large Language Model

TL;DR

This paper introduces a novel framework called SSD-LLM that uses large language models to analyze and uncover subpopulation structures within datasets, aiding various data understanding and management tasks.

Contribution

The paper proposes a new method leveraging LLMs for interpretable subpopulation structure discovery and provides workflows for multiple downstream subpopulation analysis tasks.

Findings

Effective subpopulation structure analysis using LLMs.

Unified workflow for multiple subpopulation tasks.

Improved understanding of dataset heterogeneity.

Abstract

The distribution of subpopulations is an important property hidden within a dataset. Uncovering and analyzing the subpopulation distribution within datasets provides a comprehensive understanding of the datasets, standing as a powerful tool beneficial to various downstream tasks, including Dataset Subpopulation Organization, Subpopulation Shift, and Slice Discovery. Despite its importance, there has been no work that systematically explores the subpopulation distribution of datasets to our knowledge. To address the limitation and solve all the mentioned tasks in a unified way, we introduce a novel concept of subpopulation structures to represent, analyze, and utilize subpopulation distributions within datasets. To characterize the structures in an interpretable manner, we propose the Subpopulation Structure Discovery with Large Language Models (SSD-LLM) framework, which employs world knowledge and instruction-following capabilities of Large Language Models (LLMs) to linguistically analyze informative image captions and summarize the structures. Furthermore, we propose complete workflows to address downstream tasks, named Task-specific Tuning, showcasing the application of the discovered structure to a spectrum of subpopulation-related tasks, including dataset subpopulation organization, subpopulation shift, and slice discovery. Furthermore, we propose complete workflows to address downstream tasks, named Task-specific Tuning, showcasing the application of the discovered structure to a spectrum of subpopulation-related tasks, including dataset subpopulation organization, subpopulation shift, and slice discovery.