用合成医学文本评估ChatGPT在临床营养咨询中的多语言表现:来自中亚的见解
Gulnoza Adilmetova1, Ruslan Nassyrov2, Aizhan Meyerbekova2
1Department of Biomedical Sciences, School of Medicine, Nazarbayev University, Astana, Kazakhstan.
The Journal of nutrition
|December 28, 2024
概括
像ChatGPT-4这样的大型语言模型在英语和俄语中表现适度,但由于训练数据有限,在哈萨克语中表现不佳. 针对代表性不足的语言和当地饮食习惯,需要开发定制模型.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 医疗信息学 医疗信息学
背景情况:
- 大型语言模型 (LLM) 是有希望的,但需要对代表性不足的语言和文化背景进行评估.
- 中亚语言和美食对当前的LLM成绩构成独特的挑战.
- 研究LLM在不同的语言和文化环境中的适应性至关重要.
研究的目的:
- 评估ChatGPT-4在提供英语,哈萨克语和俄语个性化营养建议方面的有效性.
- 为了在中亚语境内比较系统在这些语言中的性能.
- 评估LLM产生的饮食建议的个性化,一致性和实用性.
主要方法:
- 利用50个模拟患者个人资料,使用ChatGPT-4生成饮食建议.
- 根据个性化,一致性和实用性,使用5分利克特等级来评估响应.
- 使用Kruskal-Wallis和Dunn的后期测试来分析基于语言的表现差异.
主要成果:
- 聊天GPT-4在英语和俄语中表现适度,但哈萨克语输出不适合评估.
- 在三个语言的表现中发现了统计学上显著的差异 (P < 0.001).
- 英语和俄语的表现明显优于哈萨克语,这表明了特定语言的局限性.
结论:
- LLM的成绩受限于培训数据的可用性,特别是非英语语言.
- 目前的LLM努力为代表性不足的地区提供文化相关和实用的饮食建议.
- 建议开发专门的LLM,以解决代表性不足的语言和当地环境的局限性.
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