语音翻译用于通过大型语言模型利用的多语言医学教育
Jorge Iranzo-Sánchez1, Jaume Santamaría-Jordà1, Gerard Mas-Mollà1
1Machine Learning and Language Processing research group, Valencian Research Institute for Artificial Intelligence, Universitat Politècnica de València, Camí de Vera s/n, València, 46022, Spain.
Artificial intelligence in medicine
|May 17, 2025
概括
大型语言模型 (LLM) 显示了医疗内容的竞争性翻译质量,优于传统系统. 这项研究将LLM和其他模型适用于瘤学,为语音翻译研究释放了有价值的多语言数据集.
科学领域:
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
- 人工智能的人工智能
背景情况:
- 大型语言模型 (LLM) 在一般机器翻译 (MT) 中表现出色,但它们在专业医疗领域的表现不太清楚.
- 欧盟的Interact-Europe项目旨在为瘤专业人员提供多语言培训材料.
- 适应跨语言技术用于医学教育对于全球知识传播至关重要.
研究的目的:
- 开发,评估和部署针对瘤领域量身定制的跨语言技术.
- 评估医疗内容的不同MT模型的翻译质量,包括LLM.
- 创建和发布一个全面的语音数据集,用于培训和评估瘤学中的ASR,MT和ST模型.
主要方法:
- 自动语音识别 (ASR) 和MT模型适应瘤学领域.
- 将英语瘤学培训视频翻译成法语,西班牙语,德语和斯洛文语.
- 评估了三种MT模型类别:双语编码器-解码器,预训练的多语种编码器-解码器和只有多语种解码器的LLMs.
主要成果:
- 与传统的编码解码MT模型相比,LLM在瘤学领域表现出具有竞争力的翻译质量.
- 开发的模型被评估并部署用于多语言医学教育.
- 发布了ESO语音数据集,包含大约1000个视频 (745小时).
结论:
- 在瘤学等专业医疗领域,LLM是机器翻译的可行和有竞争力的选择.
- 公开发布ESO语音数据集将促进对医疗应用跨语言技术的进一步研究.
- 这项工作有助于通过先进的翻译技术改善获得专业医疗知识的机会.
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