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Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words. (arXiv:2209.06453v1 [cs.CL])
Sept. 15, 2022, 1:14 a.m. | Haochun Wang, Chi Liu, Nuwa Xi, Sendong Zhao, Meizhi Ju, Shiwei Zhang, Ziheng Zhang, Yefeng Zheng, Bing Qin, Ting Liu
cs.CL updates on arXiv.org arxiv.org
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 …
More from arxiv.org / cs.CL updates on arXiv.org
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