Work

LLMTaxo: Leveraging Large Language Models for Constructing Taxonomy of Factual Claims from Social Media

Year: 2025

Type: conference-paper

Abstract: With the rapid expansion of content on social media platforms, analyzing and comprehending online discourse has become increasingly complex.This paper introduces LLMTaxo, a novel framework leveraging large language models for the automated construction of taxonomies of factual claims from social media by generating topics at multiple levels of granularity.The resulting hierarchical structure significantly reduces redundancy and improves information accessibility.We also propose dedicated taxonomy evaluation metrics to enable comprehensive assessment.Evaluations conducted on three diverse datasets demonstrate LLMTaxo's effectiveness in producing clear, coherent, and comprehensive taxonomies.Among the evaluated models, GPT-4o mini consistently outperforms others across most metrics.The framework's flexibility and low reliance on manual intervention underscore its potential for broad applicability.

DOI: 10.18653/v1/2025.findings-acl.1007

Authors: Haiqi Zhang, Zhengyuan Zhu, Zeyu Zhang, Chengkai Li

Institution: The University of Texas at Arlington

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Funder: National Science Foundation

Award: POSE: Phase I: Scoping and Planning for an Open-Source Ecosystem of Machine Learning Models That Select Texts for Research Purposes

OpenAlex ID: W4412887791