Year: 2026
Type: conference-paper
Abstract: The integration of large language models (LLMs) into topic modeling has shown promise for improving topic interpretability and coherence, particularly by exploiting the generative capabilities of LLMs through instruction-based prompting.However, existing methods often incur substantial computational cost and inference complexity.To address these limitations, we propose LLMFeed, a novel framework that leverages feedback from an LLM to improve topic model optimization.Specifically, the LLM is used to evaluate topic quality, identify semantic meaning, and recommend representative topic words.These signals are then incorporated to (i) refine topic representations via Direct Preference Optimization (DPO), and (ii) guide contrastive learning for improved document-topic assignments.Extensive experiments show that LLMFeed achieves a 7.6% gain in topic coherence and over 10% improvement in doc-topic distributions on 20 Newsgroups dataset, while requiring only minutes to run, compared with hours for recent LLM-integrated topic modeling methods.The source code is available at: https://github. com/tungserver99/LLMFeed.git.
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7224415006