LLM-XTM: Enhancing Cross-Lingual Topic Models with Large Language Models

• URL: http://arxiv.org/abs/2605.03299v1
• Date: Tue, 05 May 2026 02:40:39 GMT
• Title: LLM-XTM: Enhancing Cross-Lingual Topic Models with Large Language Models
• Abstract summary: Cross-lingual topic modeling aims to discover shared semantic structures across languages.<n>Recent LLM-based refinements improve interpretability but are costly, document-level, and prone to hallucination.<n>We propose LLM-XTM, a framework that integrates LLM-guided topic refinement with self-consistency uncertainty quantification.
• Score: 12.710857972236303
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cross-lingual topic modeling aims to discover shared semantic structures across languages, yet existing models depend on sparse bilingual resources and often yield incoherent or weakly aligned topics. Recent LLM-based refinements improve interpretability but are costly, document-level, and prone to hallucination, with prior white-box approaches requiring inaccessible token probabilities. We propose LLM-XTM, a framework that integrates LLM-guided topic refinement with self-consistency uncertainty quantification, enabling black-box, stable, and scalable enhancement of cross-lingual topic models. Experiments on multilingual corpora show that LLM-XTM achieves superior topic coherence and alignment while reducing reliance on bilingual dictionaries and expensive LLM calls.

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