This paper presents UMASS_BioNLP team participation in the MEDIQA-Chat 2023
shared task for Task-A and Task-C. We focus especially on Task-C and propose a
novel LLMs cooperation system named a doctor-patient loop to generate
high-quality conversation data sets. The experiment results demonstrate that
our approaches yield reasonable performance as evaluated by automatic metrics
such as ROUGE, medical concept recall, BLEU, and Self-BLEU. Furthermore, we
conducted a comparative analysis between our proposed method and ChatGPT and
GPT-4. This analysis also investigates the potential of utilizing cooperation
LLMs to generate high-quality datasets.

本文介绍了 UMASS_BioNLP 团队参加 MEDIQA-Chat 2023 任务 A 和任务 C 的情况，并提出了一种名为医生 - 患者循环的新型 LLMs 合作系统，以生成高质量的对话数据集。通过 ROUGE、医学概念召回率、BLEU 和自身 BLEU 等自动度量标准对方法进行了评估，并对其与 ChatGPT 和 GPT-4 进行了比较分析，同时探讨了利用合作 LLMs 生成高质量数据集的潜力。