  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.
