LLM Reading Tea Leaves: Automatically Evaluating Topic Models with Large Language Models
<|reference_start|>LLM Reading Tea Leaves: Automatically Evaluating Topic Models with Large Language Models: 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.<|reference_end|>
@article{yang2024llm,
    title={LLM Reading Tea Leaves: Automatically Evaluating Topic Models with Large
  Language Models},
    author={Xiaohao Yang, He Zhao, Dinh Phung, Wray Buntine, Lan Du},
    journal={arXiv preprint arXiv:2406.09008},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.09008},
    primaryClass={cs.CL}
}