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

arXiv: https://arxiv.org/abs/2403.16248 · HTML: https://mlsanity.com/paper/2403.16248/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 47 references that are in the map.
- First posted on arXiv 24 Mar 2024.

## Builds on (most cited first, 14 of 14)

- [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)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Fine-Tuning Language Models from Human Preferences](https://mlsanity.com/paper/1909.08593/) (2019)
- [An Explanation of In-context Learning as Implicit Bayesian Inference](https://mlsanity.com/paper/2111.02080/) (2021)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Extending Context Window of Large Language Models via Positional Interpolation](https://mlsanity.com/paper/2306.15595/) (2023)
- [Benchmarking Large Language Models for News Summarization](https://mlsanity.com/paper/2301.13848/) (2023)
- [Large Language Models are Diverse Role-Players for Summarization Evaluation](https://mlsanity.com/paper/2303.15078/) (2023)
- [Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge](https://mlsanity.com/paper/1611.10277/) (2016)
- [VaxxHesitancy: A Dataset for Studying Hesitancy towards COVID-19 Vaccination on Twitter](https://mlsanity.com/paper/2301.06660/) (2023)

## Built on by (most cited first, 30 of 35)

- [Neural Topic Modeling with Large Language Models in the Loop](https://mlsanity.com/paper/2411.08534/) (2024)
- [Addressing Topic Granularity and Hallucination in Large Language Models for Topic Modelling](https://mlsanity.com/paper/2405.00611/) (2024)
- [LLMTaxo: Leveraging Large Language Models for Constructing Taxonomy of Factual Claims from Social Media](https://mlsanity.com/paper/2504.12325/) (2025)
- [Bridging the Evaluation Gap: Leveraging Large Language Models for Topic Model Evaluation](https://mlsanity.com/paper/2502.07352/) (2025)
- [Topic Over Source: The Key to Effective Data Mixing for Language Models Pre-training](https://mlsanity.com/paper/2502.16802/) (2025)
- [CEMTM: Contextual Embedding-based Multimodal Topic Modeling](https://mlsanity.com/paper/2509.11465/) (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)
- [Comprehensive Evaluation of Large Language Models for Topic Modeling](https://mlsanity.com/paper/2406.00697/) (2024)
- [CAST: Corpus-Aware Self-similarity Enhanced Topic modelling](https://mlsanity.com/paper/2410.15136/) (2024)
- [Facts are Harder Than Opinions -- A Multilingual, Comparative Analysis of LLM-Based Fact-Checking Reliability](https://mlsanity.com/paper/2506.03655/) (2025)
- [AMAQA: A Metadata-based QA Dataset for RAG Systems](https://mlsanity.com/paper/2505.13557/) (2025)
- [Capturing research literature attitude towards Sustainable Development Goals: an LLM-based topic modeling approach](https://mlsanity.com/paper/2411.02943/) (2024)
- [MALTopic: Multi-Agent LLM Topic Modeling Framework](https://mlsanity.com/paper/2601.15299/) (2026)
- [LLM-XTM: Enhancing Cross-Lingual Topic Models with Large Language Models](https://mlsanity.com/paper/2605.03299/) (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)
- [Contextual Embedding-based Clustering to Identify Topics for Healthcare Service Improvement](https://mlsanity.com/paper/2504.14068/) (2025)
- [HAMLET: Healthcare-focused Adaptive Multilingual Learning Embedding-based Topic Modeling](https://mlsanity.com/paper/2505.07157/) (2025)
- [Enhancing and Scaling Search Query Datasets for Recommendation Systems](https://mlsanity.com/paper/2505.11176/) (2025)
- [Unpacking Generative AI in Education: Computational Modeling of Teacher and Student Perspectives in Social Media Discourse](https://mlsanity.com/paper/2506.16412/) (2025)
- [Semantic-Augmented Latent Topic Modeling with LLM-in-the-Loop](https://mlsanity.com/paper/2507.08498/) (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)
- [Populism Meets AI: Advancing Populism Research with LLMs](https://mlsanity.com/paper/2510.07458/) (2025)
- [Topic-aware Large Language Models for Summarizing the Lived Healthcare Experiences Described in Health Stories](https://mlsanity.com/paper/2510.24765/) (2025)
- [Efficient Topic Extraction via Graph-Based Labeling: A Lightweight Alternative to Deep Models](https://mlsanity.com/paper/2511.04248/) (2025)
- [TopicProphet: Prophesies on Temporal Topic Trends and Stocks](https://mlsanity.com/paper/2512.11857/) (2025)
- [Large Language Model and Formal Concept Analysis: a comparative study for Topic Modeling](https://mlsanity.com/paper/2602.01933/) (2026)
- [Improving Topic Modeling by Distilling Soft Labels from Language Models](https://mlsanity.com/paper/2602.17907/) (2026)
- [Disentangling Similarity and Relatedness in Topic Models](https://mlsanity.com/paper/2603.10619/) (2026)
- [Human-Centric Topic Modeling with Goal-Prompted Contrastive Learning and Optimal Transport](https://mlsanity.com/paper/2604.12663/) (2026)

## Abstract (arXiv)

Topic modelling, as a well-established unsupervised technique, has found extensive use in automatically detecting significant topics within a corpus of documents. However, classic topic modelling approaches (e.g., LDA) have certain drawbacks, such as the lack of semantic understanding and the presence of overlapping topics. In this work, we investigate the untapped potential of large language models (LLMs) as an alternative for uncovering the underlying topics within extensive text corpora. To this end, we introduce a framework that prompts LLMs to generate topics from a given set of documents and establish evaluation protocols to assess the clustering efficacy of LLMs. Our findings indicate that LLMs with appropriate prompts can stand out as a viable alternative, capable of generating relevant topic titles and adhering to human guidelines to refine and merge topics. Through in-depth experiments and evaluation, we summarise the advantages and constraints of employing LLMs in topic extraction.