Summary of Apollo: a Lightweight Multilingual Medical Llm Towards Democratizing Medical Ai to 6b People, by Xidong Wang et al.
Apollo: A Lightweight Multilingual Medical LLM towards Democratizing Medical AI to 6B People
by Xidong Wang, Nuo Chen, Junyin Chen, Yidong Wang, Guorui Zhen, Chunxian Zhang, Xiangbo Wu, Yan Hu, Anningzhe Gao, Xiang Wan, Haizhou Li, Benyou Wang
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The authors aim to develop medical language models (LLMs) across six widely spoken languages to extend the reach of medical AI advancements to a broader population. They create the ApolloCorpora multilingual medical dataset and XMedBench benchmark, releasing Apollo models with varying sizes that achieve best performance among models of equivalent size. Notably, Apollo-7B is state-of-the-art for multilingual medical LLMs up to 70B. The authors also propose using lite models to improve larger model capabilities without fine-tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper aims to make medical AI more accessible by creating language models in six widely spoken languages. They create a big dataset and benchmark, then train smaller models that do well compared to others of the same size. One of these small models is especially good at understanding medical texts in many languages. The authors will share their work with others so it can be used to help people get better healthcare. |
Keywords
» Artificial intelligence » Fine tuning
