Leveraging LLMs for Text Classification—A Comprehensive Overview

Striveworks has recently explored the use of large language models (LLMs) for text classification, offering insights into both supervised and unsupervised learning methods. These models, traditionally not designed for text classification, demonstrate robust capabilities when adapted through innovative techniques such as fine-tuning and prompt engineering.

Supervised Learning: This approach involves fine-tuning pre-trained models like BERT and its derivatives (e.g., DistilBERT, RoBERTa) using methods like Low Rank Adaptation (LoRA) to specialize these LLMs for specific text classification tasks. Supervised learning with LLMs requires a significant dataset for training but yields high accuracy, making it suitable for scenarios where labeled data is abundant.

Unsupervised Learning: Alternatively, unsupervised learning leverages the inherent understanding of LLMs to classify texts without explicit fine-tuning. This method uses constructed prompts that guide the model to classify texts based on their content alone, which can be particularly advantageous when labeled data is scarce or when deployment needs to be swift.

Both methods have shown promising results in experimental setups. Supervised techniques, especially those utilizing LoRA, consistently deliver strong performance across various training sizes, achieving high F1 scores. Unsupervised methods, while challenging, can yield good results with careful prompt engineering and the use of techniques like k-shot prompting, where multiple examples are provided to guide the model.