# Prompting Large Language Models for Topic Modeling

arXiv: https://arxiv.org/abs/2312.09693 · HTML: https://mlsanity.com/paper/2312.09693/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 36 references that are in the map.
- First posted on arXiv 15 Dec 2023.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Topic Modeling in Embedding Spaces](https://mlsanity.com/paper/1907.04907/) (2019)
- [Autoencoding Variational Inference For Topic Models](https://mlsanity.com/paper/1703.01488/) (2017)
- [Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherence](https://mlsanity.com/paper/2107.02173/) (2021)
- [Tired of Topic Models? Clusters of Pretrained Word Embeddings Make for Fast and Good Topics too!](https://mlsanity.com/paper/2004.14914/) (2020)
- [Cross-lingual Contextualized Topic Models with Zero-shot Learning](https://mlsanity.com/paper/2004.07737/) (2020)
- [Probabilistic Latent Semantic Analysis](https://mlsanity.com/paper/1301.6705/) (2013)
- [Short Text Topic Modeling Techniques, Applications, and Performance: A Survey](https://mlsanity.com/paper/1904.07695/) (2019)
- [Language Models are Few-shot Learners for Prognostic Prediction](https://mlsanity.com/paper/2302.12692/) (2023)
- [Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations](https://mlsanity.com/paper/2202.04582/) (2022)
- [Improving Neural Topic Models using Knowledge Distillation](https://mlsanity.com/paper/2010.02377/) (2020)
- [Nonparametric Spherical Topic Modeling with Word Embeddings](https://mlsanity.com/paper/1604.00126/) (2016)
- [Multi-source Neural Topic Modeling in Multi-view Embedding Spaces](https://mlsanity.com/paper/2104.08551/) (2021)
- [Discovering Hidden Topical Hubs and Authorities in Online Social Networks](https://mlsanity.com/paper/1802.07022/) (2018)

## Built on by (most cited first, 25 of 25)

- [LimTopic: LLM-based Topic Modeling and Text Summarization for Analyzing Scientific Articles limitations](https://mlsanity.com/paper/2503.10658/) (2025)
- [Neural Topic Modeling with Large Language Models in the Loop](https://mlsanity.com/paper/2411.08534/) (2024)
- [Context-Aware Hierarchical Taxonomy Generation for Scientific Papers via LLM-Guided Multi-Aspect Clustering](https://mlsanity.com/paper/2509.19125/) (2025)
- [GPTopic: Dynamic and Interactive Topic Representations](https://mlsanity.com/paper/2403.03628/) (2024)
- [Figurative Archive: an open dataset and web-based application for the study of metaphor](https://mlsanity.com/paper/2503.00444/) (2025)
- [Topic Classification of Case Law Using a Large Language Model and a New Taxonomy for UK Law: AI Insights into Summary Judgment](https://mlsanity.com/paper/2405.12910/) (2024)
- [CAST: Corpus-Aware Self-similarity Enhanced Topic modelling](https://mlsanity.com/paper/2410.15136/) (2024)
- [Large Language Models for History, Philosophy, and Sociology of Science: Interpretive Uses, Methodological Challenges, and Critical Perspectives](https://mlsanity.com/paper/2506.12242/) (2025)
- [Capturing research literature attitude towards Sustainable Development Goals: an LLM-based topic modeling approach](https://mlsanity.com/paper/2411.02943/) (2024)
- [LLM-Assisted Topic Reduction for BERTopic on Social Media Data](https://mlsanity.com/paper/2509.19365/) (2025)
- [PRISM: LLM-Guided Semantic Clustering for High-Precision Topics](https://mlsanity.com/paper/2604.03180/) (2026)
- [LLM-XTM: Enhancing Cross-Lingual Topic Models with Large Language Models](https://mlsanity.com/paper/2605.03299/) (2026)
- [Going PLACES: Participatory Localized Red Teaming for Text-to-Image Safety in the Global South](https://mlsanity.com/paper/2605.19190/) (2026)
- [fLSA: Learning Semantic Structures in Document Collections Using Foundation Models](https://mlsanity.com/paper/2410.05481/) (2024)
- [Investigating the Impact of Text Summarization on Topic Modeling](https://mlsanity.com/paper/2410.09063/) (2024)
- [LITA: An Efficient LLM-assisted Iterative Topic Augmentation Framework](https://mlsanity.com/paper/2412.12459/) (2024)
- [Evaluating Negative Sampling Approaches for Neural Topic Models](https://mlsanity.com/paper/2503.18167/) (2025)
- [Unpacking Generative AI in Education: Computational Modeling of Teacher and Student Perspectives in Social Media Discourse](https://mlsanity.com/paper/2506.16412/) (2025)
- [Thematic and Task-Based Categorization of K-12 GenAI Usages with Hierarchical Topic Modeling](https://mlsanity.com/paper/2508.09997/) (2025)
- [LLM as Attention-Informed NTM and Topic Modeling as long-input Generation: Interpretability and long-Context Capability](https://mlsanity.com/paper/2510.03174/) (2025)
- [Beyond the Rabbit Hole: Mapping the Relational Harms of QAnon Radicalization](https://mlsanity.com/paper/2601.17658/) (2026)
- [Large Language Model and Formal Concept Analysis: a comparative study for Topic Modeling](https://mlsanity.com/paper/2602.01933/) (2026)
- [Agentic Clustering: Controllable Text Taxonomies via Multi-Agent Refinement](https://mlsanity.com/paper/2606.01255/) (2026)
- [AutoJourn: Multi-Perspective Summarisation, Bias Detection and Bias Neutralisation for LLM-Generated News in Automated Journalism](https://mlsanity.com/paper/2607.18983/) (2026)
- [TopicMerge: A Divide and Conquer Approach to Topic Modeling](https://mlsanity.com/paper/2610.19323/) (2026)

## Abstract (arXiv)

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.