具备态度的机器人：LLM驱动的机器人个性对动机和性能的影响
Robots with Attitudes: Influence of LLM-Driven Robot Personalities on Motivation and Performance
AI总结：
本研究探讨了LLM驱动的机器人顺从性对合作任务表现的影响，发现顺从性可提升受欢迎程度和任务表现。
摘要：
大型语言模型能够实现无剧本对话的同时保持一致的性格。在合作伙伴中，一个已知能提高任务表现的性格特征是顺从性。为了探索大型语言模型对机器人性格建模的影响，以及顺从性和非顺从性性格在合作任务中的影响，我们进行了两项研究。这包括一个在线预研究用于性格验证，以及一个基于实验室的主要研究以评估对受欢迎程度、动机和任务表现的影响。结果显示，机器人顺从性显著提高了其受欢迎程度。两种性格类型之间没有观察到内在动机的显著差异。然而，研究结果表明，表现出顺从性和开放新经验的机器人可以提高任务表现。本研究强调了使用大型语言模型进行定制机器人性格建模的优势，并提供了证据表明，精心选择的顺从机器人性格可以积极影响人类感知，并在合作场景中带来更大的成功。
英文摘要：
Large language models enable unscripted conversations while maintaining a consistent personality. One desirable personality trait in cooperative partners, known to improve task performance, is agreeableness. To explore the impact of large language models on personality modeling for robots, as well as the effect of agreeable and non-agreeable personalities in cooperative tasks, we conduct a two-part study. This includes an online pre-study for personality validation and a lab-based main study to evaluate the effects on likability, motivation, and task performance. The results demonstrate that the robot's agreeableness significantly enhances its likability. No significant difference in intrinsic motivation was observed between the two personality types. However, the findings suggest that a robot exhibiting agreeableness and openness to new experiences can enhance task performance. This study highlights the advantages of employing large language models for customized modeling of robot personalities and provides evidence that a carefully chosen agreeable robot personality can positively influence human perceptions and lead to greater success in cooperative scenarios.
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。
