@inproceedings{xu-etal-2026-towards,
title = "Towards Modern Topic Models: A Survey of Taxonomies and Paradigm Shifts from Algorithm-Centric to {LLM}-Centered Topic Analysis",
author = "Xu, Xuan and
Yang, Zhongliang and
Li, Haolun and
Tian, Rui and
Chu, Beilin and
Song, J and
Li, Yu and
Tan, Shaolin and
Zhou, Linna",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.326/",
pages = "6536--6561",
ISBN = "979-8-89176-395-1",
abstract = "LLMs have become foundational across many NLP applications, driving a shift from an algorithm-centric to a context-centric paradigm. As an important task in text mining, the landscape of topic modeling (TM) is similarly being reshaped by a growing body of LLM-driven research.We review recent TM developments and categorize existing methods into three groups: Classical Algorithm-Centric, LLM-Assisted, and LLM-Centric. For traditional algorithm-centric methods, we refine prior taxonomies and highlight recent advances. For the LLM-Assisted and LLM-Centric settings, we introduce a new taxonomy that emphasizes the role of LLMs and the design of end-to-end workflows, respectively. We examine two key transformations brought by LLM-centric TM: expanded task scope and a shift from model-level improvements to system-level engineering. We also propose a future roadmap for more optimized LLM-Centric TMs and identify ongoing critical challenges. We aim for this survey to spur closer integration between TM and LLMs and to further drive the progress of modern TM."
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<abstract>LLMs have become foundational across many NLP applications, driving a shift from an algorithm-centric to a context-centric paradigm. As an important task in text mining, the landscape of topic modeling (TM) is similarly being reshaped by a growing body of LLM-driven research.We review recent TM developments and categorize existing methods into three groups: Classical Algorithm-Centric, LLM-Assisted, and LLM-Centric. For traditional algorithm-centric methods, we refine prior taxonomies and highlight recent advances. For the LLM-Assisted and LLM-Centric settings, we introduce a new taxonomy that emphasizes the role of LLMs and the design of end-to-end workflows, respectively. We examine two key transformations brought by LLM-centric TM: expanded task scope and a shift from model-level improvements to system-level engineering. We also propose a future roadmap for more optimized LLM-Centric TMs and identify ongoing critical challenges. We aim for this survey to spur closer integration between TM and LLMs and to further drive the progress of modern TM.</abstract>
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%0 Conference Proceedings
%T Towards Modern Topic Models: A Survey of Taxonomies and Paradigm Shifts from Algorithm-Centric to LLM-Centered Topic Analysis
%A Xu, Xuan
%A Yang, Zhongliang
%A Li, Haolun
%A Tian, Rui
%A Chu, Beilin
%A Song, J.
%A Li, Yu
%A Tan, Shaolin
%A Zhou, Linna
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F xu-etal-2026-towards
%X LLMs have become foundational across many NLP applications, driving a shift from an algorithm-centric to a context-centric paradigm. As an important task in text mining, the landscape of topic modeling (TM) is similarly being reshaped by a growing body of LLM-driven research.We review recent TM developments and categorize existing methods into three groups: Classical Algorithm-Centric, LLM-Assisted, and LLM-Centric. For traditional algorithm-centric methods, we refine prior taxonomies and highlight recent advances. For the LLM-Assisted and LLM-Centric settings, we introduce a new taxonomy that emphasizes the role of LLMs and the design of end-to-end workflows, respectively. We examine two key transformations brought by LLM-centric TM: expanded task scope and a shift from model-level improvements to system-level engineering. We also propose a future roadmap for more optimized LLM-Centric TMs and identify ongoing critical challenges. We aim for this survey to spur closer integration between TM and LLMs and to further drive the progress of modern TM.
%U https://aclanthology.org/2026.findings-acl.326/
%P 6536-6561
Markdown (Informal)
[Towards Modern Topic Models: A Survey of Taxonomies and Paradigm Shifts from Algorithm-Centric to LLM-Centered Topic Analysis](https://aclanthology.org/2026.findings-acl.326/) (Xu et al., Findings 2026)
ACL
- Xuan Xu, Zhongliang Yang, Haolun Li, Rui Tian, Beilin Chu, J Song, Yu Li, Shaolin Tan, and Linna Zhou. 2026. Towards Modern Topic Models: A Survey of Taxonomies and Paradigm Shifts from Algorithm-Centric to LLM-Centered Topic Analysis. In Findings of the Association for Computational Linguistics: ACL 2026, pages 6536–6561, San Diego, California, United States. Association for Computational Linguistics.