This paper reports on the use of prompt engineering and GPT-3.5 for
biomedical query-focused multi-document summarisation. Using GPT-3.5 and
appropriate prompts, our system achieves top ROUGE-F1 results in the task of
obtaining short-paragraph-sized answers to biomedical questions in the 2023
BioASQ Challenge (BioASQ 11b). This paper confirms what has been observed in
other domains: 1) Prompts that incorporated few-shot samples generally improved
on their counterpart zero-shot variants; 2) The largest improvement was
achieved by retrieval augmented generation. The fact that these prompts allow
our top runs to rank within the top two runs of BioASQ 11b demonstrate the
power of using adequate prompts for Large Language Models in general, and
GPT-3.5 in particular, for query-focused summarisation.

使用提示工程和 GPT-3.5 进行生物医学问题聚焦多文档摘要，我们的系统在 2023BioASQ 挑战中通过 GPT-3.5 和适当的提示获得了最佳的 ROUGE-F1 结果。这篇论文证实了在其他领域观察到的事实：纳入少样本的提示通常优于对应的零样本变体；检索增强生成实现了最大的改进。这些提示使得我们的最佳运行结果在 BioASQ11b 排名前两位，证明了在一般情况下，使用适当的提示对于大语言模型以及 GPT-3.5 在问题聚焦摘要中的强大作用。