GPTopic: Dynamic and Interactive Topic Representations

2024-03-06

arXiv.org

AI Summary

Traditional topic modeling relies on static keyword lists, resulting in poor interpretability, high cognitive barriers, and limited accessibility for non-expert users. To address this, we propose an LLM-driven interactive topic modeling paradigm that integrates prompt engineering, conversational interfaces, topic embedding enhancement, and real-time response mechanisms—enabling natural-language querying, multi-turn semantic refinement, and dynamic topic explanation. This approach transcends the limitations of fixed vocabularies by supporting semantic, progressive topic decomposition. We release TopicGPT, an open-source implementation widely adopted by the research community. Empirical evaluations demonstrate substantial improvements in topic interpretability, accessibility, and analytical depth. Our framework establishes a novel pathway toward trustworthy, human-centered topic modeling—bridging the gap between statistical abstraction and domain understanding while maintaining methodological rigor.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Web Mining and Content Analysis: Topic discovery and tracking

Abstract

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.

Problem

• Top-word topic representations require expertise to interpret effectively
• Static topic models fail to capture nuanced aspects of topics
• Existing methods limit accessibility and comprehensive topic exploration

Innovation

• Leverages Large Language Models for dynamic topic representations
• Provides intuitive chat interface for interactive topic exploration
• Enables users to analyze and refine topics interactively