  Topic modeling seems to be almost synonymous with generating lists of top
words to represent topics within large text corpora. However, deducing a topic
from such list of individual terms can require substantial expertise and
experience, making topic modelling less accessible to people unfamiliar with
the particularities and pitfalls of top-word interpretation. A topic
representation limited to top-words might further fall short of offering a
comprehensive and easily accessible characterization of the various aspects,
facets and nuances a topic might have. To address these challenges, we
introduce GPTopic, a software package that leverages Large Language Models
(LLMs) to create dynamic, interactive topic representations. GPTopic provides
an intuitive chat interface for users to explore, analyze, and refine topics
interactively, making topic modeling more accessible and comprehensive. The
corresponding code is available here: https://github.com/ArikReuter/TopicGPT.
