UMASS_BioNLP at MEDIQA-Chat 2023：LLMs能否生成高质量的面向病历的医患对话？
UMASS_BioNLP at MEDIQA-Chat 2023: Can LLMs generate high-quality synthetic note-oriented doctor-patient conversations?
AI总结：
UMASS_BioNLP团队在MEDIQA-Chat 2023 Task-C中提出医患循环LLMs协作系统，生成高质量医患对话数据集，在多项自动指标上表现合理，并与ChatGPT和GPT-4对比验证了其有效性。
摘要：
本文介绍了UMASS_BioNLP团队参与MEDIQA-Chat 2023共享任务中Task-A和Task-C的情况。我们特别关注Task-C，提出了一种名为医患循环的新型LLMs协作系统，用于生成高质量对话数据集。实验结果表明，我们的方法在ROUGE、医学概念召回率、BLEU和Self-BLEU等自动评估指标上取得了合理的性能。此外，我们将所提方法与ChatGPT和GPT-4进行了比较分析。该分析还探讨了利用协作LLMs生成高质量数据集的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
