Medical mT5：一种面向医疗领域的开源多语言文本到文本大语言模型
Medical mT5: An Open-Source Multilingual Text-to-Text LLM for The Medical Domain
AI总结：
针对现有医疗领域大语言模型多聚焦英语、多语言文本到文本模型预训练数据匮乏的问题，研究团队构建了四语言最大医疗多语言语料库，训练出首个开源医疗多语言文本到文本模型Medical mT5并提出新基准，在多语言任务上表现优异。
摘要：
面向医疗应用开发的语言技术研究是当前自然语言理解与生成领域的热点。因此，近期已有多款大语言模型（LLM）被适配到医疗领域，可作为介导人机交互的工具。尽管这些大语言模型在自动化医疗文本基准上表现出具有竞争力的性能，但它们的预训练和评估都聚焦于单一语言（大多为英语）。文本到文本模型尤其如此，这类模型通常需要大量领域特定的预训练数据，而这类数据对很多语言来说往往难以获取。在本文中，我们针对这些不足，据我们所知构建了涵盖英语、法语、意大利语和西班牙语四种语言的最大规模医疗领域多语言语料库。我们使用该新语料库训练了Medical mT5——首个面向医疗领域的开源文本到文本多语言模型。此外，我们还为这四种语言提出了两个全新的评估基准，旨在推动该领域的多语言研究。综合评估结果显示，在西班牙语、法语和意大利语基准上，Medical mT5的表现优于编码器模型以及同等规模的文本到文本模型，同时在英语任务上与当前最先进的大语言模型性能相当。
英文摘要：
Research on language technology for the development of medical applications is currently a hot topic in Natural Language Understanding and Generation. Thus, a number of large language models (LLMs) have recently been adapted to the medical domain, so that they can be used as a tool for mediating in human-AI interaction. While these LLMs display competitive performance on automated medical texts benchmarks, they have been pre-trained and evaluated with a focus on a single language (English mostly). This is particularly true of text-to-text models, which typically require large amounts of domain-specific pre-training data, often not easily accessible for many languages. In this paper, we address these shortcomings by compiling, to the best of our knowledge, the largest multilingual corpus for the medical domain in four languages, namely English, French, Italian and Spanish. This new corpus has been used to train Medical mT5, the first open-source text-to-text multilingual model for the medical domain. Additionally, we present two new evaluation benchmarks for all four languages with the aim of facilitating multilingual research in this domain. A comprehensive evaluation shows that Medical mT5 outperforms both encoders and similarly sized text-to-text models for the Spanish, French, and Italian benchmarks, while being competitive with current state-of-the-art LLMs in English.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
