TopicGPT: A Prompt-based Topic Modeling Framework

Original Paper: https://arxiv.org/abs/2311.01449

By: Chau Minh Pham, Alexander Hoyle, Simeng Sun, Philip Resnik, Mohit Iyyer

Abstract:

Topic modeling is a well-established technique for exploring text corpora. Conventional topic models (e.g., LDA) represent topics as bags of words that often require "reading the tea leaves" to interpret; additionally, they offer users minimal control over the formatting and specificity of resulting topics. To tackle these issues, we introduce TopicGPT, a prompt-based framework that uses large language models (LLMs) to uncover latent topics in a text collection. TopicGPT produces topics that align better with human categorizations compared to competing methods: it achieves a harmonic mean purity of 0.74 against human-annotated Wikipedia topics compared to 0.64 for the strongest baseline. Its topics are also interpretable, dispensing with ambiguous bags of words in favor of topics with natural language labels and associated free-form descriptions. Moreover, the framework is highly adaptable, allowing users to specify constraints and modify topics without the need for model retraining. By streamlining access to high-quality and interpretable topics, TopicGPT represents a compelling, human-centered approach to topic modeling.

Summary Notes

Introducing TopicGPT - A New Era in Topic Modeling

The vast landscapes of text data hold untold stories, waiting to be discovered. Traditional topic modeling methods like Latent Dirichlet Allocation (LDA) have been pivotal in unearthing these narratives.

However, their limitations in flexibility and interpretability often stand in the way of truly user-friendly models. This is where TopicGPT steps in, utilizing large language models (LLMs) to transform topic modeling, making it more intuitive and adaptable to users' needs without the heavy computational cost of retraining.

The Challenges with Traditional Topic Modeling

Topic modeling helps uncover the hidden thematic layers within large text collections. Think of it as digging for thematic gold.

Traditional approaches, especially LDA, view topics as clusters of significant words, which can lead to ambiguous and hard-to-understand results.

Enter TopicGPT: A Leap Forward

TopicGPT is at the forefront of innovation, using the capabilities of LLMs to craft, refine, and allocate topics in a manner that aligns with human understanding and is easily adjustable by users.

It marks a significant advancement by offering a system that is not only more intuitive but also flexible.

Inside TopicGPT's Approach

TopicGPT innovates with a two-step process that combines generating new topics with assigning these topics to documents, all while providing textual evidence. This ensures topics are not only refined with user input but also remain clear and interpretable.

• Topic Generation and Refinement
• Initial Generation: It starts by generating new topics from a vast array of documents and pre-existing topics using LLMs.
• Refinement: It then merges similar topics and removes less common ones, keeping the topics relevant and focused.
• Topic Assignment
• Relevance Matching: TopicGPT assigns the most relevant topics to each new document, adapting effortlessly to new data.
• Textual Evidence: It enhances topic interpretability by providing evidence from the documents.

This method makes integrating user feedback and document analysis into topic modeling effortless, redefining adaptability and user interaction.

Breakthrough Performance

In tests with Wikipedia articles and U.S. Congressional bills, TopicGPT outperformed both traditional models like LDA and newer ones like BERTopic.

It showed better alignment with expert-labeled topics and improved the coherence and clarity of the generated topics.

Advantages of TopicGPT

• Better Topic Alignment: It mirrors expert-labeled topics closely, showing a deep understanding of text.
• Increased Interpretability and Usability: The topics are coherent and backed by textual evidence, making them user-friendly.
• Enhanced Adaptability: Users can easily modify and refine topics to meet their needs without extensive computational effort.

Looking Ahead

TopicGPT represents a significant leap in topic modeling, offering a more intuitive and efficient approach to analyzing text.

Its potential for application in multilingual corpora and reducing dependence on costly LLM APIs opens new possibilities for research and practical uses.

Get Involved

The TopicGPT code is available on GitHub, inviting the research community to explore, replicate, and build upon this innovative work.

This move highlights a commitment to open science and sets the stage for further advancements in topic modeling.

In summary, TopicGPT combines advanced AI with user-centric design to offer a fundamentally new approach to topic modeling. It promises deeper insights and a more interactive human-text relationship, paving the way for future innovations in the field.