  This paper presents an overview of the PromptCBLUE shared task
(http://cips-chip.org.cn/2023/eval1) 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.
