Large Language Models Revolutionize Text Mining with Minimal Human Effort

Transforming unstructured text into structured and meaningful forms is a fundamental step in text mining, but existing methods rely heavily on domain expertise and manual curation, making it expensive and time-consuming. A new two-phase framework, TnTLLM, employs Large Language Models (LLMs) to automate label generation and assignment with minimal human effort. The approach involves iterative reasoning and refinement of labels, followed by LLM-based data labeling. This framework has the potential to revolutionize text mining by providing a more efficient, effective, and scalable approach, eliminating manual curation and expertise requirements.

Can Large Language Models Revolutionize Text Mining?

The process of 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 challenge is particularly pronounced when the label space is underspecified and large-scale data annotations are unavailable. In this paper, we address these challenges with Large Language Models (LLMs), whose prompt-based interface facilitates the induction and use of large-scale pseudo labels.

The TnTLLM Framework: A Two-Phase Approach

We propose a two-phase framework, called TnTLLM, 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 multistage reasoning approach, which enables LLMs to produce and refine a label taxonomy iteratively.

This approach involves several key steps. First, the LLM is prompted with a set of seed labels, which are used as a starting point for the label generation process. The LLM then uses its language understanding capabilities to generate new labels based on the seed labels, using a combination of natural language processing (NLP) and machine learning techniques.

The generated labels are then refined through an iterative process, where the LLM is prompted with additional information and feedback to improve the accuracy and relevance of the labels. This process continues until the desired level of precision is achieved.

Phase Two: Using LLMs as Data Labelers

In the second phase, the LLM is used as a data labeler, which yields training samples for downstream machine learning models. The LLM is prompted with a set of unlabeled text data and uses its language understanding capabilities to assign labels to each sample.

This approach has several advantages over traditional methods. First, it eliminates the need for manual labeling, which can be time-consuming and expensive. Second, it allows for the use of large-scale datasets, which can provide more accurate and robust results.

The Benefits of TnTLLM

The TnTLLM framework offers several benefits over traditional text mining methods. First, it automates the process of label generation and assignment, reducing the need for manual curation and expertise. Second, it allows for the use of large-scale datasets, which can provide more accurate and robust results.

Third, the framework is highly flexible and adaptable, allowing it to be applied to a wide range of text mining tasks and domains. Finally, the use of LLMs as data labelers eliminates the need for manual labeling, reducing the time and cost associated with traditional methods.

Conclusion

In this paper, we have proposed a two-phase framework, called TnTLLM, that employs Large Language Models (LLMs) to automate the process of end-to-end label generation and assignment with minimal human effort. The framework consists of two phases: a zero-shot multistage reasoning approach for generating and refining a label taxonomy, and using LLMs as data labelers to yield training samples.

The benefits of TnTLLM include automating the process of label generation and assignment, allowing for the use of large-scale datasets, and eliminating the need for manual labeling. The framework has the potential to revolutionize text mining by providing a more efficient, effective, and scalable approach to transforming unstructured text into structured and meaningful forms.

Future Work

Future work includes exploring the application of TnTLLM to other domains and use cases, such as sentiment analysis, entity recognition, and topic modeling. Additionally, we plan to investigate the use of other LLM architectures and techniques to further improve the accuracy and efficiency of the framework.

Acknowledgments

We would like to thank our colleagues at Microsoft for their contributions to this work. We also acknowledge the support of the University of Washington and the National Science Foundation (NSF) for funding this research.