GINopic, a neural topic model that leverages a graph isomorphism network to enhance the representation of word correlations in documents, outperforms existing topic models in terms of topic coherence, diversity, and downstream task performance.
Large Language Models (LLMs) can enhance topic modeling by providing dynamic and interactive representations, making it more accessible and comprehensive.
The author introduces GPTopic, a software package that utilizes Large Language Models to create dynamic and interactive topic representations, aiming to make topic modeling more accessible and comprehensive.