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About this paper
Shuyu Chang, Rui Wang, Peng Ren, Qi Wang, Haiping Huang
A novel, model-agnostic mechanism uses Large Language Models to refine topics extracted from short texts, identifying and replacing semantically incoherent words. The approach consistently enhances topic coherence and granularity, demonstrating average increases of 21.23% in within-topic similarity and 29.05% in between-topic distance across diverse datasets and base topic models.
Key insights
- Large Language Models can effectively serve as sophisticated evaluators, mimicking human judgment to identify and correct semantic inconsistencies in topics.
- A post-processing refinement approach, rather than generative LLM-based methods, efficiently leverages LLM capabilities while maintaining compatibility with standard topic representations.
- Focusing LLM interactions on topic words, rather than entire documents, leads to significantly reduced token consumption and computational costs.
Summary by alphaXiv.
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