TnT-LLM: Text Mining at Scale with Large Language Models
(2403.12173)Abstract
Transforming unstructured text into structured and meaningful forms, organized by useful category labels, is a fundamental step in text mining for downstream analysis and application. However, most existing methods for producing label taxonomies and building text-based label classifiers still rely heavily on domain expertise and manual curation, making the process expensive and time-consuming. This is particularly challenging when the label space is under-specified and large-scale data annotations are unavailable. In this paper, we address these challenges with LLMs, whose prompt-based interface facilitates the induction and use of large-scale pseudo labels. We propose TnT-LLM, a two-phase framework that employs LLMs to automate the process of end-to-end label generation and assignment with minimal human effort for any given use-case. In the first phase, we introduce a zero-shot, multi-stage reasoning approach which enables LLMs to produce and refine a label taxonomy iteratively. In the second phase, LLMs are used as data labelers that yield training samples so that lightweight supervised classifiers can be reliably built, deployed, and served at scale. We apply TnT-LLM to the analysis of user intent and conversational domain for Bing Copilot (formerly Bing Chat), an open-domain chat-based search engine. Extensive experiments using both human and automatic evaluation metrics demonstrate that TnT-LLM generates more accurate and relevant label taxonomies when compared against state-of-the-art baselines, and achieves a favorable balance between accuracy and efficiency for classification at scale. We also share our practical experiences and insights on the challenges and opportunities of using LLMs for large-scale text mining in real-world applications.
Overview
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TnT-LLM introduces a novel framework that combines the strengths of LLMs to automate text mining tasks, focusing on generating label taxonomies and classifying text.
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The methodology of TnT-LLM comprises two phases: zero-shot taxonomy generation and text classification, with LLMs facilitating both to ensure scalability and interpretability.
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An evaluation suite assessing TnT-LLM's performance through automatic, human, and LLM-based evaluations emphasizes its efficiency in generating accurate label taxonomies and classifying text.
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The framework demonstrates significant promise in real-world applications like analyzing chat transcripts, showcasing a balanced approach between accuracy and efficiency in text mining.
Automating Text Mining with TnT-LLM: A Two-Phase Framework Leveraging LLMs
Introduction
Text mining has evolved to automate the extraction of useful information from vast collections of textual data. Traditional methods involving handcrafted taxonomies are known for their interpretability but struggle with scalability. Conversely, automatic clustering approaches offer scalability at the expense of interpretability. The paper introduces TnT-LLM, a novel framework that leverages the capabilities of LLMs to address these challenges, aiming for a balance between scalability and interpretability.
Methodology
TnT-LLM operates in two distinct phases: taxonomy generation and text classification. In the initial phase, LLMs facilitate the zero-shot generation of a label taxonomy. This process involves a multi-stage reasoning approach allowing LLMs to iteratively produce and refine a label taxonomy. Subsequently, LLMs serve as data labelers in the second phase, enabling the development of lightweight classifiers capable of scalable deployment.
Evaluation Suite
The evaluation suite for TnT-LLM incorporates deterministic automatic evaluation, human evaluation, and LLM-based evaluations to comprehensively assess the framework's performance. The suite emphasizes the coverage, accuracy, and relevance of generated label taxonomies, alongside investigating the task and annotation reliability for text classification.
Experiments
The framework is applied to analyze user intent and conversational domains within Bing Copilot's chat transcripts. Comparative analysis against state-of-the-art baselines demonstrates TnT-LLM's efficacy in generating high-quality label taxonomies and achieving a desirable balance between accuracy and efficiency in classification. The experiments also highlight practical insights into the capabilities and limitations of using LLMs for large-scale text mining.
Implications and Future Directions
The research opens several avenues for future development. The practical implications extend to real-world applications, where LLMs can significantly reduce the manual effort in text mining tasks. Theoretically, it propels the discourse on leveraging LLMs beyond traditional applications, encouraging exploration of their use in automated taxonomy generation and scalable text classification. Future work may explore enhancing the efficiency and adaptability of the framework across diverse datasets and domains.
Conclusion
TnT-LLM represents a leap forward in utilizing LLMs for text mining, offering a robust solution to the scalability and interpretability dilemma. By automating the generation of label taxonomies and enabling scalable text classification, it paves the way for advanced text mining applications capable of handling the complexities of real-world data. The framework not only showcases the potential of LLMs in automating text mining processes but also sets the stage for future innovations in the field.
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