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We show that instruction tuning using Japanese medical question-answering dataset significantly improves the ability of Japanese LLMs to solve Japanese medical license exams.
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arXiv Detail & Related papers (2024-06-21T06:04:10Z) - MedExpQA: Multilingual Benchmarking of Large Language Models for Medical Question Answering [8.110978727364397]
Large Language Models (LLMs) have the potential of facilitating the development of Artificial Intelligence technology.
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We aim to develop medical LLMs across the six most widely spoken languages, encompassing a global population of 6.1 billion.
This effort culminates in the creation of the ApolloCorpora multilingual medical dataset and the XMedBench benchmark.
We will open-source training corpora, code, model weights and evaluation benchmark.
arXiv Detail & Related papers (2024-03-06T11:56:02Z) - OpenMedLM: Prompt engineering can out-perform fine-tuning in medical
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arXiv Detail & Related papers (2024-02-05T08:25:22Z) - Zero-Shot Cross-Lingual Reranking with Large Language Models for
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arXiv Detail & Related papers (2023-12-26T18:38:54Z) - MEDITRON-70B: Scaling Medical Pretraining for Large Language Models [91.25119823784705]
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arXiv Detail & Related papers (2023-08-17T07:51:23Z)
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