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将一个大型语言模型集成到医院老年病房的社会辅助机器人中:对性能,参与度和用户感知进行两波比较研究.

Lauriane Blavette1,2,3, Sébastien Dacunha1,2,3, Xavier Alameda-Pineda4

  • 1Institut National de la Santé et de la Recherche Médicale, Optimisation Thérapeutique en Pharmacologie OTEN Unité Mixte de Recherche-Santé 1144, Université Paris Cité, Paris, France.

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概括

将大型语言模型 (LLM) 集成到社会辅助机器人 (SAR) 中,显著改善了老年护理环境中的交互成功和用户体验. 这一进步增强了老年人SARs的潜力,解决了医疗保健中的关键挑战.

关键词:
行为参与 行为参与老年医疗护理服务提供者人与机器人的交互大型语言模型.多式联运分析多式联运分析年龄较大的成年人.社交辅助机器人是一个社会辅助机器人.

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科学领域:

  • 老年医学 老年医学
  • 人与机器人的交互
  • 医疗保健中的人工智能

背景情况:

  • 在资源有限的环境中,老年人面临着复杂的医疗和心理社会需求.
  • 社会辅助机器人 (SAR) 提供实际支持,大型语言模型 (LLM) 增强了他们的对话能力.
  • SARs的可接受性取决于最小化错误和适应用户特征.

研究的目的:

  • 评估将LLM整合到医院老年病房SAR对话系统中的影响.
  • 在两个实验波中比较系统性能和交互成功.
  • 探索用户特征对性能,可接受性和可用性的影响.

主要方法:

  • 28名老年人参加了8个月的SAR单次评估.
  • 互动被记录在两个波段:基本对话系统和基于LLM的系统.
  • 系统性能,用户参与度,可接受性和可用性得到了定量和定性分析.

主要成果:

  • LLM集成显著增加了无错误交互 (27.8%至70.2%) 和交互成功 (25%至74.5%).
  • 基于LLM的系统的可接受性和可用性得分显著更高.
  • 情感参与与互动成功正相关;年龄对身体参与和接受度产生了负面影响.

结论:

  • 通过LLM集成来改善对话质量,提高了老年护理SARs中的交互成功和用户体验.
  • 行为参与受系统性能和个人用户特征的影响.
  • 多模式行为分析和自我报告措施对于临床环境中以用户为中心的机器人设计至关重要.