---
title: "LLM-XTM — paper by Xuan et al. (2026) — summary and ideas"
description: "Cross-lingual topic models try to find themes shared across languages. 69 references. Among them: 0."
authors: ["Minh Chu Xuan", "Tien-Phat Nguyen", "Linh Ngo Van", "Dinh Viet Sang", "Nguyen Thi Ngoc Diep", "Trung Le"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.170/"
doi: "10.18653/v1/2026.acl-long.170"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/xuan-2026-llm-xtm-enhancing-cross-lingual/"
---
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# LLM-XTM: Enhancing Cross-Lingual Topic Models with Large Language Models

**Authors:** [Minh Chu Xuan](https://md.research.elamur.ai/free/en/authors/minh-chu-xuan/), Tien-Phat Nguyen, [Linh Ngo Van](https://md.research.elamur.ai/free/en/authors/linh-ngo-van/), [Dinh Viet Sang](https://md.research.elamur.ai/free/en/authors/dinh-viet-sang/), [Nguyen Thi Ngoc Diep](https://md.research.elamur.ai/free/en/authors/nguyen-thi-ngoc-diep/) and [Trung Le](https://md.research.elamur.ai/free/en/authors/trung-le/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.170/) · [PDF](https://aclanthology.org/2026.acl-long.170.pdf) · [DOI 10.18653/v1/2026.acl-long.170](https://doi.org/10.18653/v1/2026.acl-long.170) · [OpenAlex](https://openalex.org/W7166890202)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Cross-lingual topic models try to find themes shared across languages. They depend on limited bilingual resources such as seed dictionaries, document embeddings or parallel corpora, so paired topics often drift apart in meaning. Earlier LLM-assisted methods either query the model for every document, which is costly and prone to hallucination, or, as in LLM-ITL, require white-box token probabilities.

LLM-XTM is a post-hoc plug-in applied to an already trained backbone (InfoCTM, NMTM, XTRA). An LLM rewrites each topic's bilingual top-word lists several times. Following the SelfCheckGPT idea, only words that recur across these sampled refinements are kept. An MMD loss then pulls the model's topic-word distributions toward this filtered set. A second, QA-inspired loss embeds documents and refined topics with BGE-M3 and minimizes KL divergence between the model's document-topic proportions and similarity-based targets.

On English–Chinese and English–Japanese corpora (Amazon Review, ECNews, Rakuten Amazon), the authors report better coherence and cross-lingual alignment than the baselines. The method needs only black-box LLM access and relies less on dictionaries. This is useful for multilingual corpora that lack strong bilingual resources.

## Key points
- Two added objectives on top of a VAE-based backbone:
  - an MMD loss (Gaussian kernel on cosine distances) aligning topic-word distributions with LLM-refined words;
  - a KL(θ_d ∥ θ̂_d) document-level alignment loss using BGE-M3 embeddings.
- Uncertainty is estimated by self-consistency across multiple sampled refinement paths, not by token probabilities. This makes the method usable with closed-source LLMs.
- Sensitivity study on Amazon Review with NMTM, at refinement frequency f = 8:
  - Raising refinement rounds R from 1 to 5 lifted CNPMI from 0.0482 to 0.0562 (+16.6%). TU rose only from 0.600 to 0.627.
  - Beyond R = 5 the gains are small, so the authors recommend R ≈ 5–7.
  - More frequent LLM calls raise CNPMI but lower TU, a trade-off between coherence and topic uniqueness.
- An LLM-as-judge evaluation (1–3 scale, mean of four runs) on XTRA showed gains in both intra-lingual coherence and cross-lingual alignment. The alignment gains were larger where the baseline was unstable.
- Stated limitations:
  - The method can refine topics but cannot rescue a failed backbone.
  - Repeated API calls add latency.
- Code is released at github.com/tienphat140205/LLM-XTM.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Minh Chu Xuan, Tien-Phat Nguyen, Linh Ngo Van, Dinh Viet Sang, Nguyen Thi Ngoc Diep and Trung Le. 2026. LLM-XTM: Enhancing Cross-Lingual Topic Models with Large Language Models. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{xuan2026llm,
  title = {{LLM-XTM: Enhancing Cross-Lingual Topic Models with Large Language Models}},
  author = {Minh Chu Xuan, Tien-Phat Nguyen, Linh Ngo Van, Dinh Viet Sang, Nguyen Thi Ngoc Diep and Trung Le},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.170},
  url = {https://doi.org/10.18653/v1/2026.acl-long.170}
}
```

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*Updated 31 October 2026*