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BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text
March 28, 2024, 4:48 a.m. | Elliot Bolton, Abhinav Venigalla, Michihiro Yasunaga, David Hall, Betty Xiong, Tony Lee, Roxana Daneshjou, Jonathan Frankle, Percy Liang, Michael Carb
cs.CL updates on arXiv.org arxiv.org
Abstract: Models such as GPT-4 and Med-PaLM 2 have demonstrated impressive performance on a wide variety of biomedical NLP tasks. However, these models have hundreds of billions of parameters, are computationally expensive to run, require users to send their input data over the internet, and are trained on unknown data sources. Can smaller, more targeted models compete? To address this question, we build and release BioMedLM, a 2.7 billion parameter GPT-style autoregressive model trained exclusively on …
arxiv biomedical cs.ai cs.cl language language model text type
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