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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2023
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| Online Access: | https://arxiv.org/abs/2304.14670 |
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| _version_ | 1866914725327011840 |
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| author | Wang, Jiaqi Shi, Enze Yu, Sigang Wu, Zihao Ma, Chong Dai, Haixing Yang, Qiushi Kang, Yanqing Wu, Jinru Hu, Huawen Yue, Chenxi Zhang, Haiyang Liu, Yiheng Pan, Yi Liu, Zhengliang Sun, Lichao Li, Xiang Ge, Bao Jiang, Xi Zhu, Dajiang Yuan, Yixuan Shen, Dinggang Liu, Tianming Zhang, Shu |
| author_facet | Wang, Jiaqi Shi, Enze Yu, Sigang Wu, Zihao Ma, Chong Dai, Haixing Yang, Qiushi Kang, Yanqing Wu, Jinru Hu, Huawen Yue, Chenxi Zhang, Haiyang Liu, Yiheng Pan, Yi Liu, Zhengliang Sun, Lichao Li, Xiang Ge, Bao Jiang, Xi Zhu, Dajiang Yuan, Yixuan Shen, Dinggang Liu, Tianming Zhang, Shu |
| contents | Prompt engineering is a critical technique in the field of natural language processing that involves designing and optimizing the prompts used to input information into models, aiming to enhance their performance on specific tasks. With the recent advancements in large language models, prompt engineering has shown significant superiority across various domains and has become increasingly important in the healthcare domain. However, there is a lack of comprehensive reviews specifically focusing on prompt engineering in the medical field. This review will introduce the latest advances in prompt engineering in the field of natural language processing for the medical field. First, we will provide the development of prompt engineering and emphasize its significant contributions to healthcare natural language processing applications such as question-answering systems, text summarization, and machine translation. With the continuous improvement of general large language models, the importance of prompt engineering in the healthcare domain is becoming increasingly prominent. The aim of this article is to provide useful resources and bridges for healthcare natural language processing researchers to better explore the application of prompt engineering in this field. We hope that this review can provide new ideas and inspire for research and application in medical natural language processing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_14670 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Prompt Engineering for Healthcare: Methodologies and Applications Wang, Jiaqi Shi, Enze Yu, Sigang Wu, Zihao Ma, Chong Dai, Haixing Yang, Qiushi Kang, Yanqing Wu, Jinru Hu, Huawen Yue, Chenxi Zhang, Haiyang Liu, Yiheng Pan, Yi Liu, Zhengliang Sun, Lichao Li, Xiang Ge, Bao Jiang, Xi Zhu, Dajiang Yuan, Yixuan Shen, Dinggang Liu, Tianming Zhang, Shu Artificial Intelligence Prompt engineering is a critical technique in the field of natural language processing that involves designing and optimizing the prompts used to input information into models, aiming to enhance their performance on specific tasks. With the recent advancements in large language models, prompt engineering has shown significant superiority across various domains and has become increasingly important in the healthcare domain. However, there is a lack of comprehensive reviews specifically focusing on prompt engineering in the medical field. This review will introduce the latest advances in prompt engineering in the field of natural language processing for the medical field. First, we will provide the development of prompt engineering and emphasize its significant contributions to healthcare natural language processing applications such as question-answering systems, text summarization, and machine translation. With the continuous improvement of general large language models, the importance of prompt engineering in the healthcare domain is becoming increasingly prominent. The aim of this article is to provide useful resources and bridges for healthcare natural language processing researchers to better explore the application of prompt engineering in this field. We hope that this review can provide new ideas and inspire for research and application in medical natural language processing. |
| title | Prompt Engineering for Healthcare: Methodologies and Applications |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2304.14670 |