Revisiting Automated Topic Model Evaluation with Large Language Models

Topic models help make sense of large text collections. Automatically evaluating their output and determining the optimal number of topics are both longstanding challenges, with no effective automated solutions to date. This paper evaluates the effectiveness of large language models (LLMs) for these tasks. We find that LLMs appropriately assess the resulting topics, correlating more strongly with human judgments than existing automated metrics. However, the type of evaluation task matters — LLMs correlate better with coherence ratings of word sets than on a word intrusion task. We find that LLMs can also guide users toward a reasonable number of topics. In actual applications, topic models are typically used to answer a research question related to a collection of texts. We can incorporate this research question in the prompt to the LLM, which helps estimate the optimal number of topics