title: BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text
summary: 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 PubMed abstracts and full articles. When
  fine-tuned, BioMedLM can produce strong multiple-choice biomedical
  question-answering results competitive with much larger models, such as
  achieving a score of 57.3% on MedMCQA (dev) and 69.0% on the MMLU Medical
  Genetics exam. BioMedLM can also be fine-tuned to produce useful answers to
  patient questions on medical topics. This demonstrates that smaller models can
  potentially serve as transparent, privacy-preserving, economical and
  environmentally friendly foundations for particular NLP applications, such as
  in biomedicine. The model is available on the Hugging Face Hub:
  https://huggingface.co/stanford-crfm/BioMedLM.
paper_authors._id: 6604d1a9596db3db5dc65fac
paper_authors.name: Elliot Bolton
paper_authors._id: 6604d1a9596db3db5dc65fad
paper_authors.name: Abhinav Venigalla
paper_authors._id: 6604d1a9596db3db5dc65fae
paper_authors.name: Michihiro Yasunaga
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-21T16:39:31.050Z
paper_authors._id: 621e9388345a1d9ab65391c3
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/621e9388345a1d9ab65391c3/RxurNzyAWJOUdgeSHQi1R.jpeg
paper_authors.fullname: Michihiro Yasunaga
paper_authors.type: user
paper_authors.user: michiyasunaga
paper_authors._id: 6604d1a9596db3db5dc65faf
paper_authors.name: David Hall
paper_authors._id: 6604d1a9596db3db5dc65fb0
paper_authors.name: Betty Xiong
paper_authors._id: 6604d1a9596db3db5dc65fb1
paper_authors.name: Tony Lee
paper_authors._id: 6604d1a9596db3db5dc65fb2
paper_authors.name: Roxana Daneshjou
paper_authors._id: 6604d1a9596db3db5dc65fb3
paper_authors.name: Jonathan Frankle
paper_authors._id: 6604d1a9596db3db5dc65fb4
paper_authors.name: Percy Liang
paper_authors._id: 6604d1a9596db3db5dc65fb5
paper_authors.name: Michael Carbin
paper_authors._id: 6604d1a9596db3db5dc65fb6
paper_authors.name: Christopher D. Manning
paper_title: BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text
paper_summary: 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 PubMed abstracts and full articles. When
  fine-tuned, BioMedLM can produce strong multiple-choice biomedical
  question-answering results competitive with much larger models, such as
  achieving a score of 57.3% on MedMCQA (dev) and 69.0% on the MMLU Medical
  Genetics exam. BioMedLM can also be fine-tuned to produce useful answers to
  patient questions on medical topics. This demonstrates that smaller models can
  potentially serve as transparent, privacy-preserving, economical and
  environmentally friendly foundations for particular NLP applications, such as
  in biomedicine. The model is available on the Hugging Face Hub:
  https://huggingface.co/stanford-crfm/BioMedLM.
paper_ai_keywords: autoregressive model PubMed abstracts PubMed full articles multiple-choice biomedical question-answering MedMCQA MMLU Medical Genetics exam fine-tuned Hugging Face Hub