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arXiv 2401.03217cs.ROcs.HC

理解大语言模型（LLM）驱动的人机交互

Understanding Large-Language Model (LLM)-powered Human-Robot Interaction

University of Wisconsin–Madison（威斯康星大学麦迪逊分校）

Callie Y. Kim, Christine P. Lee, Bilge Mutlu

备注：
10 pages, 4 figures. Callie Y. Kim and Christine P. Lee contributed equally to the work. To be published in Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (HRI '24), March 11--14, 2024, Boulder, CO, USA
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AI总结：

本研究通过用户实验比较了LLM驱动的社交机器人与文本、语音智能体在对话任务中的表现，揭示了LLM机器人在非语言线索和建立联系上的优势及逻辑沟通上的不足，并提出了设计启示。

摘要：

大语言模型（LLMs）在改善人机交互方面具有重大前景，能够在各种任务和领域中处理多样且开放的用户请求，提供高级对话技能和多功能性。尽管有改变人机交互的潜力，但对于在机器人中使用LLMs的独特设计要求知之甚少，这些要求可能不同于文本和语音交互，并因任务和情境而异。为了更好地理解这些要求，我们进行了一项用户研究（n = 32），将LLM驱动的社交机器人与基于文本和语音的智能体进行比较，分析了对话任务中基于任务的要求，包括选择、生成、执行和协商。研究结果表明，LLM驱动的机器人提高了对复杂非语言线索的期望，在建立联系和深思熟虑方面表现出色，但在逻辑沟通方面存在不足，可能引发焦虑。我们为集成LLMs的机器人以及微调LLMs以用于机器人提供了设计启示。

英文摘要：

Large-language models (LLMs) hold significant promise in improving human-robot interaction, offering advanced conversational skills and versatility in managing diverse, open-ended user requests in various tasks and domains. Despite the potential to transform human-robot interaction, very little is known about the distinctive design requirements for utilizing LLMs in robots, which may differ from text and voice interaction and vary by task and context. To better understand these requirements, we conducted a user study (n = 32) comparing an LLM-powered social robot against text- and voice-based agents, analyzing task-based requirements in conversational tasks, including choose, generate, execute, and negotiate. Our findings show that LLM-powered robots elevate expectations for sophisticated non-verbal cues and excel in connection-building and deliberation, but fall short in logical communication and may induce anxiety. We provide design implications both for robots integrating LLMs and for fine-tuning LLMs for use with robots.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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