微调大型语言模型,以改善低资源语言的健康沟通
Nhat Bui1, Giang Nguyen1, Nguyen Nguyen1
1School of Science, Engineering and Technology, RMIT University, Ho Chi Minh City, Vietnam.
Computer methods and programs in biomedicine
|February 23, 2025
概括
这项研究为越南医疗保健信息微调了开源大型语言模型 (LLM),改善了资源较少的环境中的可访问性. 微调模型表现出增强的性能,解决了关键的医疗保健沟通缺口.
科学领域:
- 自然语言处理自然语言处理.
- 医疗保健中的人工智能
- 低资源的语言技术
背景情况:
- 在像越南语这样的低资源语言中,医疗保健信息的可访问性受到限制.
- 大型语言模型 (LLM) 提供了潜力,但需要特定领域的适应.
- 发展中国家在利用先进的人工智能促进健康方面面临着独特的挑战.
研究的目的:
- 为越南医疗保健信息微调LLM开发一种方法.
- 加强越南的医疗沟通和信息可访问性.
- 根据越南医疗保健的特定语言和领域要求,调整LLM.
主要方法:
- 选择了三个开源的LLM作为基础模型.
- 编制了来自各种来源的约337,000个即时响应对的越南医疗保健数据集.
- 使用低级调整 (LoRA) 和量化LoRA (QLoRA) 技术的微调模型.
- 使用BertScore,Rouge-L和LLM-as-a-Judge进行评估的表现.
主要成果:
- 微调模型显示,在所有指标上,与基准模型相比,性能显著改善.
- 证明了LoRA和QLoRA在特定领域的LLM适应方面的有效性.
- 强调了越南语医疗保健沟通改善的潜力.
结论:
- 微调的LLM对于医疗保健中的越南语等低资源语言是有效的.
- 显著的计算能力和成本给发展中国家带来了挑战.
- 需要进一步的举措,通过可访问的人工智能来促进全球健康公平.
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