Prompt-based fine-tuning for pre-trained models has proven effective for many natural language processing tasks under few-shot settings in general domain. However, tuning with prompt in biomedical domain has not been investigated thoroughly. Biomedical words are often rare in general domain, but quite ubiquitous in biomedical contexts, which dramatically deteriorates the performance of pre-trained models on downstream biomedical applications even after fine-tuning, especially in low-resource scenarios. We propose a simple yet effective approach to helping models learn rare biomedical words during tuning with prompt. Experimental results show that our method can achieve up to 6% improvement in biomedical natural language inference task without any extra parameters or training steps using few-shot vanilla prompt settings.
@article{arxiv.2209.06453,
title = {Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words},
author = {Haochun Wang and Chi Liu and Nuwa Xi and Sendong Zhao and Meizhi Ju and Shiwei Zhang and Ziheng Zhang and Yefeng Zheng and Bing Qin and Ting Liu},
journal= {arXiv preprint arXiv:2209.06453},
year = {2023}
}