Towards Building Multilingual Language Model for Medicine
This work addresses the need for accessible, multilingual medical AI tools for linguistically diverse populations, though it is incremental as it builds on existing LLM frameworks.
The authors tackled the problem of limited multilingual medical language models by constructing a 25.5B-token multilingual medical corpus (MMedC) and a benchmark (MMedBench), resulting in MMed-Llama 3, an 8B-parameter model that outperforms other open-source models and rivals GPT-4 on these benchmarks.
The development of open-source, multilingual medical language models can benefit a wide, linguistically diverse audience from different regions. To promote this domain, we present contributions from the following: First, we construct a multilingual medical corpus, containing approximately 25.5B tokens encompassing 6 main languages, termed as MMedC, enabling auto-regressive domain adaptation for general LLMs; Second, to monitor the development of multilingual medical LLMs, we propose a multilingual medical multi-choice question-answering benchmark with rationale, termed as MMedBench; Third, we have assessed a number of open-source large language models (LLMs) on our benchmark, along with those further auto-regressive trained on MMedC. Our final model, MMed-Llama 3, with only 8B parameters, achieves superior performance compared to all other open-source models on both MMedBench and English benchmarks, even rivaling GPT-4. In conclusion, in this work, we present a large-scale corpus, a benchmark and a series of models to support the development of multilingual medical LLMs.