向着人工智能驱动的应用:为HRI和HCI开发个性化的LLM
Khashayar Ghamati1,2, Maryam Banitalebi Dehkordi1,2, Abolfazl Zaraki1,2
1School of Physics, Engineering and Computer Science (SPECS), University of Hertfordshire, Hatfield AL10 9AB, UK.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
我们推出了一种新的个性化大型语言模型 (PLLM) 代理来增强人机器人和人机器人之间的交互. 这种人工智能适应用户和环境,改善人工智能个性化和现实世界的应用.
科学领域:
- 人工智能的人工智能
- 人与计算机的交互
- 机器人技术 机器人技术 机器人技术
背景情况:
- 当前的大型语言模型 (LLM) 在动态用户适应方面面临挑战.
- 现有的研究往往忽视了用户特定的背景和环境演变.
- 在人机器人和人机交互中需要适应性AI.
研究的目的:
- 提出一个新的个性化大语言模型 (PLLM) 代理.
- 加强LLM在人机交互 (HRI) 和人机交互 (HCI) 的整合和适应.
- 为 LLMs 解决动态用户和环境适应方面的差距.
主要方法:
- 开发了一种方法来个性化LLM,使用特定领域的数据.
- 使用NeuroSenseEEG数据集进行测试和验证.
- 启用了针对AI适应性的个性化数据解释.
主要成果:
- 在现实世界HRI场景中展示了PLLM代理的可用性.
- 展示了在医疗保健,教育和辅助技术等多个领域的应用性.
- 验证了个性化数据解释和AI适应性的方法.
结论:
- 拟议的PLLM代理代表了AI适应性和个性化方面的重大进步.
- 这项工作为以用户为中心的AI应用程序做出了贡献,并解决了诸如可通用性和数据隐私等道德考虑.
- 这些发现支持PLLMs在各个领域提供实质性益处的潜力.
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