  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 (Words
Agreement with Language Model), a new evaluation method for topic modeling that
comprehensively 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 will be available at
https://github.com/Xiaohao-Yang/Topic_Model_Evaluation, which can be integrated
with many widely used topic models.
