Abstract
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 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.
Overview
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Introduces a new topic modeling approach, PromptTopic, which employs LLMs like ChatGPT and LLaMa.
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Enhances topic discovery by considering both word and sentence-level semantics without extensive manual parameter tuning.
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Outperforms state-of-the-art baselines in qualitative assessment and maintains consistency in automatic metrics.
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Utilizes Prompt-Based Matching (PBM) and Word Similarity Matching (WSM) to merge overlapping topics into comprehensive ones.
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Facilitates efficient exploration of unstructured information and is particularly effective with diverse datasets, including short texts.
Introduction to Topic Modeling
Topic modeling is a statistical method used extensively in text mining and information retrieval. It serves to identify latent patterns and themes within large sets of textual data, facilitating efficient exploration of unstructured information. However, conventional topic modeling techniques can sometimes fall short, especially when working with short texts or lacking sentence-level contextual awareness.
The PromptTopic Strategy
The study introduces PromptTopic, an innovative topic modeling approach that employs the power of LLMs like ChatGPT and LLaMa. Departing from traditional techniques that primarily focus on word-level analysis, PromptTopic promises to enhance topic discovery by incorporating both word and sentence-level semantics, without the need for extensive manual parameter tuning. This allows for an intuitive and context-rich extraction of topics from text documents.
Experimental Insights
PromptTopic was rigorously tested against state-of-the-art baselines on several datasets, showing its adeptness in accurately and cohesively uncovering topics. Notably, its implementation considering various datasets demonstrates that PromptTopic maintains consistency with other methods in automatic metrics and surpasses them in generating meaningful topics upon qualitative assessment. Additionally, the method was tuned to merge overlapping topics into more comprehensive ones using techniques like Prompt-Based Matching (PBM) and Word Similarity Matching (WSM), each offering a distinct strategy for topic condensation.
Outcomes and Practical Implications
The results revealed that PromptTopic is effective in processing diverse datasets, including those with short texts, by leveraging in-context learning facilitated through LLMs. It bypasses the labor-intensive fine-tuning phase typically associated with topic modeling, offering a streamlined experience. With its inventive prompt-engineering technique, PromptTopic marks a significant advancement in topic modeling, potentially serving various applications in research and data analysis sectors that rely on the extraction of information from large textual archives.
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