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arXiv AI By Myeongjun Erik Jang, Antonios Georgiadis, Sae Young Moon, Fran Silavong

Segment-Level Agentic Topic Modeling for Improved Data Exploration and Resource Efficiency

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The paper introduces SeLATM, a framework that improves topic modeling by generating topics at the segment level and refining them through agentic feedback loops. This approach addresses limitations of LLM-based topic assignment methods, such as the inability to produce topic distributions, overly broad or narrow topics, and high resource consumption. Experiments on multiple datasets show that SeLATM reduces LLM resource usage while maintaining superior performance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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