mlsanityData to 7 Oct 2026

GPTopic: Dynamic and Interactive Topic Representations

arXiv 2403.03628 ↗First posted 6 Mar 2024cs.CLcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 10 of its 36 references that are in the map. First posted on arXiv 6 Mar 2024.

Builds on

10 of 36 references.

References · 10

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 2,950
  3. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
  4. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
  5. 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 779
  6. 2019Topic Modeling in Embedding Spacescited by 224
  7. 2020Top2Vec: Distributed Representations of Topicscited by 124
  8. 2017Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a surveycited by 90
  9. 2020Tired of Topic Models? Clusters of Pretrained Word Embeddings Make for Fast and Good Topics too!cited by 73
  10. 2023Prompting Large Language Models for Topic Modelingcited by 24

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 3.

7 papers.

  1. 2025Belief-Calibrated Multi-Agent Consensus Seeking for Complex NLP Taskscited by 1
  2. 2025Evaluating Negative Sampling Approaches for Neural Topic Modelscited by 0
  3. 2025Contextual Embedding-based Clustering to Identify Topics for Healthcare Service Improvementcited by 0
  4. 2025Listening Between the Lines: Decoding Podcast Narratives with Language Modelingcited by 0
  5. 2026LLM-Augmented Changepoint Detection: A Framework for Ensemble Detection and Automated Explanationcited by 0
  6. 2026Beyond Semantics: Modeling Factual and Affective Perceptual Experiences from Vision-Language Datacited by 0
  7. 2026Advanced Topic Modeling Techniques for Categorizing Software Vulnerabilitiescited by 0