@inproceedings{xuan-etal-2026-llm,
    title = "{LLM}-{XTM}: Enhancing Cross-Lingual Topic Models with Large Language Models",
    author = "Xuan, Minh Chu  and
      Nguyen, Tien-Phat  and
      Van, Linh Ngo  and
      Sang, Dinh Viet  and
      Diep, Nguyen Thi Ngoc  and
      Le, Trung",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.170/",
    pages = "3719--3737",
    ISBN = "979-8-89176-390-6",
    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."
}