Recent advancements in healthcare harness multilingual language models like GPT-4, MedPalm-2, and open-source alternatives such as Llama 2. However, their effectiveness in non-English medical queries needs improvement. Shanghai researchers developed MMedLM 2, a multilingual medical language model outperforming others, benefiting diverse linguistic communities. The study emphasizes the significance of comprehensive evaluation metrics and auto-regressive training using MMedC.
Advancements in Healthcare with Multilingual Language Models
Recent advancements in healthcare are leveraging multilingual language models (LLMs) such as GPT-4, MedPalm-2, and open-source alternatives like Llama 2. These models excel in English-language applications and even surpass closed-source counterparts sometimes. However, their effectiveness in non-English medical queries needs improvement, hampering their impact on linguistically diverse communities.
Introducing MMedLM 2: A Practical Solution
Researchers from Shanghai Jiao Tong University and Shanghai AI Laboratory have developed MMedLM 2, an open-source, multilingual language model tailored for medicine with 7B parameters. This model outperforms other open-source models, rivaling GPT-4, especially in non-English medical inquiries.
Key Components and Benefits
The study introduces the Multilingual Medical Corpus (MMedC) of over 25.5 billion tokens spanning six languages, enabling auto-regressive training, and the Multilingual Medical Benchmark (MMedBench) for evaluating models’ abilities. These components significantly enhance language models’ performance in medical contexts, addressing language barriers and cultural sensitivities.
Auto-regressive training on MMedC and incorporating diverse data sources from medical content and textbooks enhance language model performance. Additionally, integrating rationale during fine-tuning improves task-specific performance, while stronger foundational LLMs yield better results.
Practical Applications and Availability
MMedLM 2 is publicly available, offering significant research and clinical implications, especially in addressing language barriers, cultural sensitivities, and educational needs. The researchers plan to release the dataset, codebase, and models to facilitate future research.
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