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GPTopic: Dynamic and Interactive Topic Representations

Arik Reuter, Bishnu Khadka, Anton Thielmann, Christoph Weisser, Sebastian Fischer, Benjamin Säfken

TL;DR

This work addresses the limited interpretability and static nature of conventional top-word topic representations by introducing GPTopic, an LLM-assisted framework for dynamic, interactive topic representations. GPTopic combines embedding-based topic extraction (UMAP for dimensionality reduction and HDBSCAN for clustering with optional fixed-topic merges), with LLM-generated topic names and descriptions informed by large top-word sets. It features a chat-based interface and Retrieval-Augmented Generation to support question answering, topic comparisons, and fine-grained topic refinements (splitting, merging, deleting) driven by user prompts. The approach aims to democratize topic analysis, making it more accessible and adaptable across domains, with a public implementation available on GitHub. Overall, GPTopic enhances interpretability, interactivity, and usability of topic representations in large text corpora.

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.

GPTopic: Dynamic and Interactive Topic Representations

TL;DR

This work addresses the limited interpretability and static nature of conventional top-word topic representations by introducing GPTopic, an LLM-assisted framework for dynamic, interactive topic representations. GPTopic combines embedding-based topic extraction (UMAP for dimensionality reduction and HDBSCAN for clustering with optional fixed-topic merges), with LLM-generated topic names and descriptions informed by large top-word sets. It features a chat-based interface and Retrieval-Augmented Generation to support question answering, topic comparisons, and fine-grained topic refinements (splitting, merging, deleting) driven by user prompts. The approach aims to democratize topic analysis, making it more accessible and adaptable across domains, with a public implementation available on GitHub. Overall, GPTopic enhances interpretability, interactivity, and usability of topic representations in large text corpora.

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.
Paper Structure (11 sections, 2 figures)

This paper contains 11 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Topic Extraction
  3. Topic Naming and Descriptions
  4. Interaction with Topics
  5. Questions
  6. Modifications
  7. A Chat-based Interface
  8. Example Usage
  9. Limitations and Shortcomings
  10. Related Work
  11. Conclusion

Figures (2)

  • Figure 1: The GPTopic package allows a user to dynamically interact with a topic. A topic can be thought of as a structure defined by its documents, title, description, and top-words. Users cannot only read the topic's description but also ask questions and interactively modify the topic. Note that beyond what this figure shows, interactions on a more global level, e.g., comparisons of topics, are also possible.
  • Figure 2: The chat-based interface for GPTopic is implemented by processing a user-defined prompt with an LLM. The LLM then decides which function to call. The result of this function call is processed with a further LLM-prompt and the final result is output.s.