Summary of Biomedlm: a 2.7b Parameter Language Model Trained on Biomedical Text, by Elliot Bolton et al.
BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text
by Elliot Bolton, Abhinav Venigalla, Michihiro Yasunaga, David Hall, Betty Xiong, Tony Lee, Roxana Daneshjou, Jonathan Frankle, Percy Liang, Michael Carbin, Christopher D. Manning
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the possibility of using smaller, targeted models for biomedical natural language processing (NLP) tasks. The authors 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 achieves strong multiple-choice question-answering results competitive with larger models, such as MedMCQA (dev) and the MMLU Medical Genetics exam. The model can also be used to produce useful answers to patient questions on medical topics. This demonstrates that smaller models can serve as transparent, privacy-preserving, economical, and environmentally friendly foundations for specific NLP applications, like biomedicine. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary BioMedLM is a new kind of computer program that helps doctors and researchers understand medical texts better. It’s like a super smart research assistant! The model was trained on lots of PubMed articles and can answer questions about medicine really well. In fact, it’s almost as good as bigger models that need way more computing power. BioMedLM is special because it’s private and doesn’t use the internet to work. This makes it a great tool for medical research. |
Keywords
» Artificial intelligence » Autoregressive » Gpt » Natural language processing » Nlp » Question answering
