GPTopic: Dynamic and Interactive Topic Representations

Arik Reuter, Bishnu Khadka, Anton Thielmann, Christoph Weisser, Sebastian Fischer +1 more
3/6/2024
cs.CL

Official code: ArikReuter/TopicGPT (116 stars, MIT, last commit 2025-09-19).

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.

View on arXivView PDF

Code Implementations(1)

ArikReuter/TopicGPTOfficial100%
MIT

TopicGPT allows to integrate the benefits of LLMs into Topic Modelling

11618PythonSep 18, 20231 years agoMIT
chatgptgptgpt-3gpt-4natural-language-processing+6 more

Similar Papers

topicwizard -- a Modern, Model-agnostic Framework for Topic Model Visualization and Interpretation

84% match2025

A Large Language Model Guided Topic Refinement Mechanism for Short Text Modeling

82% match1 repo322024

Comprehensive Evaluation of Large Language Models for Topic Modeling

82% match2024

Addressing Topic Granularity and Hallucination in Large Language Models for Topic Modelling

81% match1 repo62024

Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling

81% match1 repo32024

LimTopic: LLM-based Topic Modeling and Text Summarization for Analyzing Scientific Articles limitations

81% match1 repo52025

Cite this paper

@article{reuter2024gptopic,
  title  = {GPTopic: Dynamic and Interactive Topic Representations},
  author = {Arik Reuter and Bishnu Khadka and Anton Thielmann and Christoph Weisser and Sebastian Fischer and Benjamin Säfken},
  year   = {2024},
  eprint = {2403.03628},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2403.03628v3}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2403.03628.svg)](https://codeofpaper.com/paper/2403.03628)

Discussion