# TR-GPT-CF: A Topic Refinement Method Using GPT and Coherence Filtering
## 1 Introduction and State of the Art
Furthermore, BERTopic utilized advanced language models such as BERT [25] to improve the semantic comprehension of text, yielding highly coherent and contextually pertinent topics, thus rendering it exceptionally successful for intricate datasets with nuanced language. However, despite their wide range of applications, these topic modeling systems frequently suffer from low coherence and inconsistent topic generation [26]. These issues are especially evident in traditional models, which have several major flaws. First, the themes provided frequently lack coherence, making it difficult for users to derive significant conclusions [27]. Additionally, executing a single model numerous times with the same input document can produce different topics [28]. Furthermore, these models fail to capture the contextual nuance of language, which is critical for comprehending the deeper meanings inherent in texts [29]. Finally, the restrictions in interpretability for real-world applications reduce their usefulness, as stakeholders want clear and actionable outputs in order to make informed decisions [9]. These issues could raise problems such as semantic irrelevance, where topics contain words that do not meaningfully relate [30]; lack of thematic unity, where topics are broad or ambiguous and fail to represent a coherence subject [31]; and the inclusion of outliers, where irrelevant or rare words skew topic interpretation. This issue is especially prevalent in models like LDA, where the number of topics must be predefined, and improper specifications can lead to either simplistic structures or excessive topic fragmentation. Prior work has demonstrated that inadequate topic selection can result in uninformative and duplicated topics, allowing outlier words to influence topic meaning [32]. Additionally, the identified topics may not necessarily correlate with human evaluations of topic quality and may be perceived as poor from the point of view of an end user [27; 33]. Numerous studies [34; 35; 36; 37; 38] have been performed in order to overcome these problems, with a substantial emphasis placed on the process of refining the topics that are generated as a result of topic modeling methodologies. Among the five studies, refs. [35; 38] are model agnostic, which means that they do not rely on any one topic modeling technique. This model-agnostic feature enables these methods to be versatile and applicable across various contexts utilizing different topic modeling techniques. Study [34] explores how non-expert users engage with and refine topic models, revealing the gap between user requirements and the capabilities of present interactive topic modeling systems. To learn how non-experts assess, interpret, and update topic models, the researchers performed two user studies--an in-person interview and an online crowdsourced study. They identified numerous topic model modifications that users wanted. Users often sought to add words to explain and accentuate a topic's theme. To improve topic clarity and relevancy, and irrelevant or generic terms were deleted. Changes to the word order were also considered to properly reflect the topic's concept. Users also consolidated similar topics to remove repetition and split broad topics into more specialized ones to make the model more detailed and useful. The study highlights the necessity of developing topic modeling tools that are more intuitive and correspond more closely with the methods by which non-experts typically evaluate and adjust topics. This approach enhances the usability of topic models while simultaneously improving their quality, aligning them more closely with user's understanding and needs. An innovative method proposed by [35] addresses refining the topic model. This approach incorporates word-embedding projections with interactive user input within the context of a visual analytics framework. The method uses word embedding projections to build a visual representation of the themes.