mlsanityData to 7 Oct 2026

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

arXiv 2605.03299 ↗First posted 5 May 2026cs.CLcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 30 of its 69 references that are in the map. First posted on arXiv 5 May 2026.

Builds on

30 of 69 references.

References · 30

  1. 2015Learning Transferable Features with Deep Adaptation Networkscited by 1,323
  2. 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 779
  3. 2018Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyondcited by 434
  4. 2016Kernel Mean Embedding of Distributions: A Review and Beyondcited by 360
  5. 2015Generative Moment Matching Networkscited by 252
  6. 2017MMD GAN: Towards Deeper Understanding of Moment Matching Networkcited by 251
  7. 2019Topic Modeling in Embedding Spacescited by 224
  8. 2017Autoencoding Variational Inference For Topic Modelscited by 168
  9. 2017Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptationcited by 151
  10. 2017A Survey Of Cross-lingual Word Embedding Modelscited by 142
  11. 2020Top2Vec: Distributed Representations of Topicscited by 124
  12. 2020Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherencecited by 121
18 more
  1. 2020Cross-lingual Contextualized Topic Models with Zero-shot Learningcited by 66
  2. 2019Topic Modeling with Wasserstein Autoencoderscited by 53
  3. 2020Neural Topic Model via Optimal Transportcited by 50
  4. 2021Contrastive Learning for Neural Topic Modelcited by 48
  5. 2023Effective Neural Topic Modeling with Embedding Clustering Regularizationcited by 46
  6. 2024Large Language Models Offer an Alternative to the Traditional Approach of Topic Modellingcited by 35
  7. 2020Improving Neural Topic Models using Knowledge Distillationcited by 25
  8. 2023InfoCTM: A Mutual Information Maximization Perspective of Cross-Lingual Topic Modelingcited by 24
  9. 2023Prompting Large Language Models for Topic Modelingcited by 24
  10. 2024Topic Modeling as Multi-Objective Contrastive Optimizationcited by 12
  11. 2024Neural Topic Modeling with Large Language Models in the Loopcited by 10
  12. 2024NeuroMax: Enhancing Neural Topic Modeling via Maximizing Mutual Information and Group Topic Regularizationcited by 9
  13. 2021Fine-tuning Encoders for Improved Monolingual and Zero-shot Polylingual Neural Topic Modelingcited by 8
  14. 2024GloCOM: A Short Text Neural Topic Model via Global Clustering Contextcited by 4
  15. 2024Refining Dimensions for Improving Clustering-based Cross-lingual Topic Modelscited by 3
  16. 2025XTRA: Cross-Lingual Topic Modeling with Topic and Representation Alignmentscited by 3
  17. 2026GloCTM: Cross-Lingual Topic Modeling via a Global Context Spacecited by 3
  18. 2018Lessons from the Bible on Modern Topics: Low-Resource Multilingual Topic Model Evaluationcited by 2

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Citing papers per year: 2026: 1.

1 paper.

  1. 2026TokenRatio: Principled Token-Level Preference Optimization via Ratio Matchingcited by 0

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