大型语言模型和同理心:系统审查
Vera Sorin1, Dana Brin2,3, Yiftach Barash2,3,4
1Department of Radiology, Mayo Clinic, Rochester, MN, United States.
Journal of medical Internet research
|December 11, 2024
概括
大型语言模型 (LLM) 在表现同情心,识别情绪和提供支持方面表现有前途. 虽然LLM表现出认知同情,但需要进一步的研究来完善他们的表现和评估方法.
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.
- 计算语言学 计算语言学
背景情况:
- 共感在人类互动和医疗保健中至关重要,传统上被认为是人类独特的特征.
- 大型语言模型 (LLM) 是先进的人工智能系统,其同情能力受到质疑.
研究的目的:
- 审查现有的关于大语言模型 (LLM) 展示同情能力的文献.
- 综合在各种背景下与同理心相关的任务中LLM绩效的发现.
主要方法:
- 从2022年12月到2024年2月,在多个学术数据库 (MEDLINE,Google Scholar,PsyArXiv,medRxiv,arXiv) 进行了全面的文献搜索.
- 包括英语全长出版物评估LLM输出中的同情心,不包括专门关注同情心的研究.
主要成果:
- 从2023年开始的12项研究符合标准,主要是评估ChatGPT-3.5并将其与其他LLM如GPT-4和LLaMA进行比较.
- LLM 展示了同理心的元素,包括情感识别和情感支持,其中一些在特定任务中表现优于人类.
- 评估方法各不相同,包括自动指标 (例如,蓝色,红色) 和人类主观评估,并指出了诸如重复措辞和快速敏感性等局限性.
结论:
- LLM表现出认知同情,识别情绪并提供支持性反应,使他们更接近类似人类的互动.
- 在LLM同理心的进步扩大他们的潜在应用在需要情绪智能的领域.
- 对软技能的LLM绩效和评估策略进行进一步改进是必要的.
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