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

Commercial LLMs GPT-3.5-Turbo and GPT-4 demonstrated competitive performance in BioASQ answer generation using zero-shot learning, with GPT-3.5-Turbo outperforming in specific scenarios.

Year
2023
Venue
arXiv 2023
Authors
2
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Abstract onlyARXIV-DEFAULT

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arxiv.org/abs/2306.16108v2ARXIV-DEFAULT
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Abstract

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.

Authors

2