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

Xiaohao Yang; He Zhao; Dinh Phung; Wray Buntine; Lan Du

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arXiv · 2406.09008

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

Xiaohao Yang·He Zhao·Dinh Phung·Wray Buntine·Lan Du

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Abstract

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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Xiaohao Yang, He Zhao, Dinh Phung, Wray Buntine, Lan Du. 2024-06-13. LLM Reading Tea Leaves: Automatically Evaluating Topic Models with Large Language Models. https://arxiv.org/abs/2406.09008

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