This paper presents an overview of the PromptCBLUE shared task
(this http URL) held in the CHIP-2023 Conference. This
shared task reformualtes the CBLUE benchmark, and provide a good testbed for
Chinese open-domain or medical-domain large language models (LLMs) in general
medical natural language processing. Two different tracks are held: (a) prompt
tuning track, investigating the multitask prompt tuning of LLMs, (b) probing
the in-context learning capabilities of open-sourced LLMs. Many teams from both
the industry and academia participated in the shared tasks, and the top teams
achieved amazing test results. This paper describes the tasks, the datasets,
evaluation metrics, and the top systems for both tasks. Finally, the paper
summarizes the techniques and results of the evaluation of the various
approaches explored by the participating teams.

该研究论文介绍了 PromptCBLUE 共享任务，该任务重塑了 CBLUE 基准，并为中文开放领域或医学领域的大型语言模型（LLMs）提供了一个良好的测试平台，探索了 LLMs 的多任务提示优化和开源 LLMs 的上下文学习能力调查，并总结了各参与团队在不同方法上的技术和结果评估。