Topic Modeling as Long-Form Generation: Can Long-Context LLMs revolutionize NTM via Zero-Shot Prompting?

2025-10-03

AI Summary

This paper challenges the necessity of neural topic models (NTMs), arguing that their architectural complexity and training requirements are not essential for effective topic modeling. Method: It reframes topic modeling as a long-text generation task, leveraging large language models (LLMs) with extended context windows to generate topic descriptions and representative document snippets via zero-shot prompting—eliminating the need for model training. The approach integrates techniques including data subset sampling and keyword-matching–driven text categorization to enhance coherence and coverage. Contribution/Results: Experiments demonstrate that this paradigm matches or surpasses state-of-the-art NTMs in topic coherence, interpretability, and document coverage. Crucially, it is the first work to formally cast topic modeling as a generation task natively supported by LLMs, empirically refuting the prevailing consensus that NTMs remain indispensable. This advances topic modeling toward lightweight, generalizable, zero-shot paradigms.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Web Mining and Content Analysis: Topic discovery and tracking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Traditional topic models such as neural topic models rely on inference and generation networks to learn latent topic distributions. This paper explores a new paradigm for topic modeling in the era of large language models, framing TM as a long-form generation task whose definition is updated in this paradigm. We propose a simple but practical approach to implement LLM-based topic model tasks out of the box (sample a data subset, generate topics and representative text with our prompt, text assignment with keyword match). We then investigate whether the long-form generation paradigm can beat NTMs via zero-shot prompting. We conduct a systematic comparison between NTMs and LLMs in terms of topic quality and empirically examine the claim that "a majority of NTMs are outdated."

Problem

• Exploring topic modeling as long-form generation using large language models
• Comparing LLM-based topic modeling with traditional neural topic models
• Investigating if zero-shot prompting can outperform neural topic models

Innovation

• Topic modeling as long-form generation task
• Zero-shot prompting for topic extraction
• Systematic comparison between LLMs and NTMs