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- W4362679551 abstract "Recent advances in large language models (LLMs) have shown impressive ability in biomedical question-answering, but have not been adequately investigated for more specific biomedical applications. This study investigates the performance of LLMs such as the ChatGPT family of models (GPT-3.5s, GPT-4) in biomedical tasks beyond question-answering. Because no patient data can be passed to the OpenAI API public interface, we evaluated model performance with over 10000 samples as proxies for two fundamental tasks in the clinical domain - classification and reasoning. The first task is classifying whether statements of clinical and policy recommendations in scientific literature constitute health advice. The second task is causal relation detection from the biomedical literature. We compared LLMs with simpler models, such as bag-of-words (BoW) with logistic regression, and fine-tuned BioBERT models. Despite the excitement around viral ChatGPT, we found that fine-tuning for two fundamental NLP tasks remained the best strategy. The simple BoW model performed on par with the most complex LLM prompting. Prompt engineering required significant investment." @default.
- W4362679551 created "2023-04-07" @default.
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- W4362679551 date "2023-04-05" @default.
- W4362679551 modified "2023-09-26" @default.
- W4362679551 title "Evaluation of ChatGPT Family of Models for Biomedical Reasoning and Classification" @default.
- W4362679551 doi "https://doi.org/10.48550/arxiv.2304.02496" @default.
- W4362679551 hasPublicationYear "2023" @default.
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