  Crafting effective topic models for brief texts, like tweets and news
headlines, is essential for capturing the swift shifts in social dynamics.
Traditional topic models, however, often fall short in accurately representing
the semantic intricacies of short texts due to their brevity and lack of
contextual data. In our study, we harness the advanced capabilities of Large
Language Models (LLMs) to introduce a novel approach termed "Topic Refinement".
This approach does not directly involve itself in the initial modeling of
topics but focuses on improving topics after they have been mined. By employing
prompt engineering, we direct LLMs to eliminate off-topic words within a given
topic, ensuring that only contextually relevant words are preserved or
substituted with ones that fit better semantically. This method emulates
human-like scrutiny and improvement of topics, thereby elevating the semantic
quality of the topics generated by various models. Our comprehensive evaluation
across three unique datasets has shown that our topic refinement approach
significantly enhances the semantic coherence of topics.
