PromptMix

PromptMix is a cutting-edge method designed to enhance the performance of text classification models, particularly when training data is limited. By leveraging the power of large language models (LLMs) like GPT-3, PromptMix generates and refines augmented data to improve the accuracy and efficiency of smaller, more cost-effective classifiers. This innovative approach addresses the challenges of text classification by focusing on generating and relabeling borderline examples, ensuring higher-quality training data. PromptMix's two-step process involves creating challenging text augmentations near class boundaries and then relabeling these augmentations using a prompting-based LLM classifier. This method has been evaluated on various datasets, including Banking77, TREC6, Subjectivity (SUBJ), and Twitter Complaints, demonstrating significant improvements in both 2-shot and zero-shot settings. The code for PromptMix is available on GitHub, making it accessible for researchers and developers to explore and implement. With its ability to transfer knowledge from massive LLMs to smaller classifiers, PromptMix offers a promising solution for enhancing text classification tasks.