用大模型提升跨语言主题模型，更准更省资源。
LLM-XTM: Enhancing Cross-Lingual Topic Models with Large Language Models
- 用大模型引导主题优化，黑盒操作无须词频概率
- 主题一致性与跨语对齐效果更好，减少对双语词典依赖
- 适合需要低成本高精度跨语言分析的研究者
跨语言主题建模旨在发现多语言间的共享语义结构，但现有方法依赖稀疏的双语资源，常产生不连贯或弱对齐的主题。近期基于大模型的改进虽提升了可解释性，但代价高昂、仅限文档级处理，且易产生幻觉；以往白盒方法还需难以获取的词元概率。我们提出 LLM-XTM 框架，融合大模型引导的主题精炼与自一致不确定性量化，实现黑盒、稳定且可扩展的跨语言主题模型增强。在多语言语料上的实验表明，LLM-XTM 在提升主题一致性与跨语对齐效果的同时，降低了对双语词典和昂贵大模型调用的依赖。
原文摘要 · Abstract (English)
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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