We assessed the performance of commercial Large Language Models (LLMs)
GPT-3.5-Turbo and GPT-4 on tasks from the 2023 BioASQ challenge. In Task 11b
Phase B, which is focused on answer generation, both models demonstrated
competitive abilities with leading systems. Remarkably, they achieved this with
simple zero-shot learning, grounded with relevant snippets. Even without
relevant snippets, their performance was decent, though not on par with the
best systems. Interestingly, the older and cheaper GPT-3.5-Turbo system was
able to compete with GPT-4 in the grounded Q&A setting on factoid and list
answers. In Task 11b Phase A, focusing on retrieval, query expansion through
zero-shot learning improved performance, but the models fell short compared to
other systems. The code needed to rerun these experiments is available through
GitHub.

本文评估了商业 Large Language Models (LLMs) GPT-3.5-Turbo 和 GPT-4 在 2023 BioASQ 挑战的任务中的表现，其中 0-shot learning 和相关段落达到了竞争水平。

ChatGPT 模型在生物医学任务中的零样本性能探究

Is ChatGPT a Biomedical Expert? -- Exploring the Zero-Shot Performance  of Current GPT Models in Biomedical Tasks

We present AUEB's submissions to the BioASQ 6 document and snippet retrieval
tasks (parts of Task 6b, Phase A). Our models use novel extensions to deep
learning architectures that operate solely over the text of the query and
candidate document/snippets. Our systems scored at the top or near the top for
all batches of the challenge, highlighting the effectiveness of deep learning
for these tasks.

本研究介绍了 AUEB 团队在 BioASQ 6 文件和片段检索任务中的提交结果，提出了对深度学习结构的创新扩展，这些结构仅对查询文本和候选文档 / 片段进行操作，并且在挑战的所有批次中均得分最高或接近最高，凸显了深度学习在此类任务中的有效性。