arXiv:2605.03299v2 Announce Type: replace 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
The proliferation of Large Language Models (LLMs) and the increasing demand for globalized understanding are driving innovation in cross-lingual capabilities, prompting solutions to existing model limitations.
This development addresses key limitations in cross-lingual AI, improving the accuracy and efficiency of understanding shared semantic structures across diverse languages, which is critical for global information processing and communication.
LLM-XTM offers a more stable and cost-effective method for cross-lingual topic modeling, moving beyond reliance on sparse bilingual resources and expensive document-level LLM-based approaches, making advanced cross-lingual AI more accessible and reliable.
- · AI researchers
- · Multinational corporations
- · Translation services
- · Global intelligence platforms
- · Legacy cross-lingual modeling techniques
- · Companies reliant on expensive, less accurate cross-lingual tools
Improved accuracy and efficiency in cross-lingual information retrieval and analysis will become widely available.
This could lead to accelerated development of multilingual AI applications and services, reducing language barriers in various domains.
Enhanced cross-lingual understanding might foster greater global collaboration and reduce miscommunication, impacting international relations and commerce.
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