  Topic modeling is a widely used technique for revealing underlying thematic
structures within textual data. However, existing models have certain
limitations, particularly when dealing with short text datasets that lack
co-occurring words. Moreover, these models often neglect sentence-level
semantics, focusing primarily on token-level semantics. In this paper, we
propose PromptTopic, a novel topic modeling approach that harnesses the
advanced language understanding of large language models (LLMs) to address
these challenges. It involves extracting topics at the sentence level from
individual documents, then aggregating and condensing these topics into a
predefined quantity, ultimately providing coherent topics for texts of varying
lengths. This approach eliminates the need for manual parameter tuning and
improves the quality of extracted topics. We benchmark PromptTopic against the
state-of-the-art baselines on three vastly diverse datasets, establishing its
proficiency in discovering meaningful topics. Furthermore, qualitative analysis
showcases PromptTopic's ability to uncover relevant topics in multiple
datasets.
