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.
LLM as Attention-Informed NTM and Topic Modeling as long-input Generation: Interpretability and long-Context Capability
Beijing University of Posts and Telecommunications（北京邮电大学）；University of Science and Technology Beijing（北京科技大学）；Beijing Value Simplex Technology Co. Ltd（北京价值简单科技有限公司）；Zhongguancun Laboratory（中关村实验室）
A Large Language Model Guided Topic Refinement Mechanism for Short Text Modeling
Nanjing University of Posts and Telecommunications（南京邮电大学）；Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks（江苏省无线传感器网络高技术研究重点实验室）
Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning
Peking University（北京大学）；Software Engineering Institution（软件工程研究院）；Key Lab of High Confidence Software Technologies, Ministry of Education（高可信软件技术重点实验室（教育部））；Center on Frontiers of Computing Studies（计算机前沿研究中心）；National Engineering Research Center of Software Engineering（国家软件工程工程研究中心）
Human-Centric Topic Modeling with Goal-Prompted Contrastive Learning and Optimal Transport
School of Computer Science, Nanjing University of Posts and Telecommunications（南京邮电大学计算机科学学院）；School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机科学与信息工程学院）；School of Electronic Engineering and Computer Science, Queen Mary University of London（伦敦玛丽女王大学电子工程与计算机科学学院）；Department of Engineering Science, University of Oxford（牛津大学工程科学系）
Label Semantic Expansion via Label Guided Neural Topic Modeling
Division of Artificial Intelligence, Lingnan University（岭南大学人工智能学院）；School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；School of Science and Technology, Hong Kong Metropolitan University（香港都会大学科技学院）
