加强超市机器人互动:一个公平的多层次的LLM对话界面,用于处理各种客户意图
Chandran Nandkumar1, Luka Peternel1
1Department of Cognitive Robotics, Delft University of Technology, Delft, Netherlands.
Frontiers in robotics and AI
|May 14, 2025
概括
本研究介绍了一种新的超市多大语言模型 (LLM) 聊天机器人,在用户满意度和效率方面表现优于GPT-4 Turbo. 它还将OpenAI的Whisper确定为各种用户的顶级语音识别模型.
科学领域:
- 人与计算机的交互
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 像GPT这样的当前可定制系统在响应时间,战略控制和定制应用程序的成本效益方面面临挑战.
- 在零售环境中需要公平,高效和个性化的对话代理.
研究的目的:
- 为超市购物者设计和评估基于语音的接口系统.
- 为了比较语音识别技术的准确性,跨性别和语言.
- 开发和评估一个新的多LLM聊天机器人框架与专门的GPT模型相比.
主要方法:
- 对四种现成的语音识别技术 (性别/语言特定) 的比较分析.
- 开发和评估一个多LLM超市聊天机器人框架.
- 使用人工社会代理人问卷 (ASAQ) 和定性反进行绩效比较.
- 基于聊天机器人响应的超市机器人导航方法.
主要成果:
- OpenAI的Whisper在不同性别和语言中展示了卓越的语音识别准确性.
- 拟议的多LLM聊天机器人在性能,用户满意度,用户-代理合作伙伴关系和自我形象增强方面明显优于GPT-4 Turbo模型.
- 所有13个评估的方面都出现了改善,在四个关键领域具有统计学意义.
结论:
- 多LLM方法在超市语音接口等专业应用中提供了优势,而不是单一,强大的模型.
- 开发的系统增强了用户体验,可以与机器人导航集成,用于产品检索.
- 这项研究提倡使用多个专门的小型模型来提高效率和定制性.
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