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arXiv · 2209.06453

Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words

Haochun Wang·Chi Liu·Nuwa Xi·Sendong Zhao·Meizhi Ju·Shiwei Zhang·Ziheng Zhang·Yefeng Zheng·Bing Qin·Ting Liu
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Abstract

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.

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Haochun Wang, Chi Liu, Nuwa Xi, Sendong Zhao, Meizhi Ju, Shiwei Zhang, Ziheng Zhang, Yefeng Zheng, Bing Qin, Ting Liu. 2022-09-14. Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words. https://arxiv.org/abs/2209.06453

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