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arXiv · 2605.03299

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

Minh Chu Xuan·Tien-Phat Nguyen·Linh Ngo Van·Dinh Viet Sang·Nguyen Thi Ngoc Diep·Trung Le
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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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Minh Chu Xuan, Tien-Phat Nguyen, Linh Ngo Van, Dinh Viet Sang, Nguyen Thi Ngoc Diep, Trung Le. 2026-05-05. LLM-XTM: Enhancing Cross-Lingual Topic Models with Large Language Models. https://arxiv.org/abs/2605.03299

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