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We evaluate four state-of-the-art instruction-tuned large language models(LLMs) -- ChatGPT, Flan-T5 UL2, Tk-Instruct, and Alpaca -- on a set of 13real-world clinical and biomedical natural language processing (NLP) tasks inEnglish, such as named-entity recognition (NER), question-answering (QA),relation extraction (RE), etc. Our overall results demonstrate that theevaluated LLMs begin to approach performance of state-of-the-art models inzero- and few-shot scenarios for most tasks, and particularly well for the QAtask, even though they have never seen examples from these tasks before.However, we observed that the classification and RE tasks perform below whatcan be achieved with a specifically trained model for the medical field, suchas PubMedBERT. Finally, we noted that no LLM outperforms all the others on allthe studied tasks, with some models being better suited for certain tasks thanothers.

A Zero-shot and Few-shot Study of Instruction-Finetuned Large Language Models Applied to Clinical and Biomedical Tasks