Using LLM-Based Approaches to Enhance and Automate Topic Labeling

• URL: http://arxiv.org/abs/2502.18469v1
• Date: Mon, 03 Feb 2025 08:07:05 GMT
• Title: Using LLM-Based Approaches to Enhance and Automate Topic Labeling
• Authors: Trishia Khandelwal,
• Abstract summary: This study explores the use of Large Language Models (LLMs) to automate and enhance topic labeling.<n>After applying BERTopic for topic modeling, we explore different approaches to select keywords and document summaries within each topic.<n>Each approach prioritizes different aspects, such as dominant themes or diversity, to assess their impact on label quality.
• Score: 13.581341206178525
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Topic modeling has become a crucial method for analyzing text data, particularly for extracting meaningful insights from large collections of documents. However, the output of these models typically consists of lists of keywords that require manual interpretation for precise labeling. This study explores the use of Large Language Models (LLMs) to automate and enhance topic labeling by generating more meaningful and contextually appropriate labels. After applying BERTopic for topic modeling, we explore different approaches to select keywords and document summaries within each topic, which are then fed into an LLM to generate labels. Each approach prioritizes different aspects, such as dominant themes or diversity, to assess their impact on label quality. Additionally, recognizing the lack of quantitative methods for evaluating topic labels, we propose a novel metric that measures how semantically representative a label is of all documents within a topic.

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