Summary of Biomistral: a Collection Of Open-source Pretrained Large Language Models For Medical Domains, by Yanis Labrak et al.
BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains
by Yanis Labrak, Adrien Bazoge, Emmanuel Morin, Pierre-Antoine Gourraud, Mickael Rouvier, Richard Dufour
First submitted to arxiv on: 15 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 BioMistral, an open-source Large Language Model (LLM) tailored for the biomedical domain, outperforms existing medical models on 10 established question-answering tasks in English. Built upon Mistral, BioMistral was pre-trained on PubMed Central and demonstrates competitive performance against proprietary counterparts. Additionally, lightweight models were obtained through quantization and model merging approaches. The multilingual generalization of medical LLMs is assessed by automatically translating and evaluating the benchmark into 7 other languages, marking a first large-scale evaluation in this domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary BioMistral is a new type of language model that’s super good at answering medical questions! It was made specifically for doctors and researchers to use. This paper shows how BioMistral does way better than other similar models on lots of tricky medical question-answering tasks. They also made smaller versions of the model by making it use less computer power or combining it with other models. The best part is that they tested BioMistral in many different languages, which helps make sure it can be used by people who don’t speak English. |
Keywords
* Artificial intelligence * Generalization * Language model * Large language model * Quantization * Question answering
