Core Concepts
Large Language Models (LLMs) can automate and scale text mining processes efficiently, generating accurate label taxonomies and enabling lightweight classifiers for large-scale applications.
Abstract
The content discusses the use of Large Language Models (LLMs) in automating text mining processes, specifically focusing on taxonomy generation and text classification. The TnT-LLM framework is proposed to address challenges in producing label taxonomies and building classifiers, showcasing its effectiveness through experiments on user intent and conversational domain analysis. The framework leverages LLMs to generate pseudo labels for training samples, leading to reliable classifiers with high scalability and model transparency.
1. Introduction
Importance of structured text analysis.
Challenges in manual curation for taxonomy generation.
Proposal of TnT-LLM framework using LLMs.
2. Taxonomy Generation with LLMs
Two-phase framework explained.
Comparison with baseline methods.
Evaluation results showing superiority of TnT-LLM.
3. Text Classification with Lightweight Classifiers
Use of LLM-generated labels for training classifiers.
Performance comparison with GPT-4 as a classifier.
Results indicating competitive performance of distilled classifiers.
4. Impact and Future Directions
Potential impact on AI technologies in text mining.
Challenges and future directions for improving efficiency and evaluation methods.
Stats
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