Work

Towards Modern Topic Models: A Survey of Taxonomies and Paradigm Shifts from Algorithm-Centric to LLM-Centered Topic Analysis

Year: 2026

Type: conference-paper

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 LLMdriven 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 systemlevel 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. 1 * These authors contributed equally.

Source: Findings of the Association for Computational Linguistics: ACL (Findings of ACL)

DOI: 10.18653/v1/2026.findings-acl.326