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LLM Reading Tea Leaves: Automatically Evaluating Topic Models with Large Language Models

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arxiv 2406.09008 v2 pith:2FA35XST submitted 2024-06-13 cs.CL

Xiaohao Yang , He Zhao , Dinh Phung , Wray Buntine , Lan Du This is my paper · ORCID
classification cs.CL
keywords topicmodelmodelsevaluationlanguagemodelingqualitydifferent
verification ladder T0 review T1 audit T2 compute T3 formal
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Topic modeling has been a widely used tool for unsupervised text analysis. However, comprehensive evaluations of a topic model remain challenging. Existing evaluation methods are either less comparable across different models (e.g., perplexity) or focus on only one specific aspect of a model (e.g., topic quality or document representation quality) at a time, which is insufficient to reflect the overall model performance. In this paper, we propose WALM (Word Agreement with Language Model), a new evaluation method for topic modeling that considers the semantic quality of document representations and topics in a joint manner, leveraging the power of Large Language Models (LLMs). With extensive experiments involving different types of topic models, WALM is shown to align with human judgment and can serve as a complementary evaluation method to the existing ones, bringing a new perspective to topic modeling. Our software package is available at https://github.com/Xiaohao-Yang/Topic_Model_Evaluation.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Bridging the Evaluation Gap: Leveraging Large Language Models for Topic Model Evaluation

    cs.CL 2025-02 conditional novelty 6.0 of 10

    LLM-based metrics for coherence, repetitiveness, diversity, and topic-document alignment rate topic models, but scores shift substantially depending on which LLM does the judging.

  2. Advanced Topic Modeling Techniques for Categorizing Software Vulnerabilities

    cs.CR 2026-07 reject novelty 2.5 of 10

    Existing embedding-based topic models produce interpretable clusters on Cisco vulnerability Threat text, but without quantitative coherence scores, baselines, or downstream prioritization metrics.

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