TnT-LLM: LLMs for Automated Text Taxonomy and Classification

This paper presents TnT-LLM, a framework leveraging LLMs to automate large-scale text analysis, including label generation and efficient classifier training.

Table of Links

Abstract and 1 Introduction

2 Related Work

3 Method and 3.1 Phase 1: Taxonomy Generation

3.2 Phase 2: LLM-Augmented Text Classification

4 Evaluation Suite and 4.1 Phase 1 Evaluation Strategies

4.2 Phase 2 Evaluation Strategies

5 Experiments and 5.1 Data

5.2 Taxonomy Generation

5.3 LLM-Augmented Text Classification

5.4 Summary of Findings and Suggestions

6 Discussion and Future Work, and References

A. Taxonomies

B. Additional Results

C. Implementation Details

D. Prompt Templates

3.2 Phase 2: LLM-Augmented Text Classification

After the taxonomy is finalized, we next train a text classifier that can be reliably deployed to perform label assignments at very large-scale and in real-time. Following recent work that shows the strengths ...