SeLATM：面向数据探索与资源效率的片段级智能体主题建模

Computer Science > Artificial Intelligence arXiv:2609.31460 (cs) [Submitted on 25 Sep 2026] Title:Segment-Level Agentic Topic Modeling for Improved Data Exploration and Resource Efficiency Authors:Myeongjun Erik Jang, Antonios Georgiadis, Sae Young Moon, Fran Silavong View a PDF of the paper titled Segment-Level Agentic Topic Modeling for Improved Data Exploration and Resource Efficiency, by Myeongjun Erik Jang and 3 other authors View PDF HTML (experimental) Abstract:Topic modeling is an effective technique for discovering hidden themes within documents and is widely used in text mining and data analysis across a variety of industry sectors. Recently, large language model (LLM)-based topic models have been emerged that prompt LLMs to generate topics then assign the topics to documents, producing more natural and human-readable topics than conventional topic modeling algorithms. However, the nature of topic assignment process causes certain drawbacks, such as the incapability to produce topic distributions over a document, too broad or narrow topics, and high resource consumption, which increases with the number and length of of documents being assigned topics. These issues are particularly critical for industrial applications, which require high-quality, in-depth analysis and the processing of large volumes of documents. In this context, this paper introduces a framework called SeLATM, which addresses these concerns by employing segment-level topic generation and topic refinement through agentic feedback loops. Experimental results on various datasets demonstrate that SeLATM significantly reduces the LLM resources compared to methods based on topic assignment process, while maintaining superior performance. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2609.31460 [cs.AI] (or arXiv:2609.31460v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2609.31460 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Myeongjun Erik Jang [view email] [v1] Fri, 25 Sep 2026 16:11:42 UTC (345 KB) Full-text links: Access Paper: View a PDF of the paper titled Segment-Level Agentic Topic Modeling for Improved Data Exploration and Resource Efficiency, by Myeongjun Erik Jang and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI new | recent | 2026-09 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)