给聊天机器人设计性格提示，避免胡说八道和误导用户。
Why did My Robot Just Change Personality? Prompting Guidelines for a Grounded Robot Persona in LLM-Based HRI
- 提出8个组件的结构化提示模板，明确机器人行为边界。
- 专家调研发现机器人性格难理解，需可解释的设定。
- 适合做对话机器人的研究者和伦理安全设计者参考。
大语言模型（LLM）越来越多用于社交机器人交互，但人机交互（HRI）中的提示设计仍缺乏规范。这导致机器人可能表现出幻觉能力、行为边界模糊及误导性人格。本文基于对先前LLM-HRI研究的回顾，并结合在IEEE RO-MAN 2025会议上举办的Robo-Identity工作坊中收集的27位专家的调查与讨论数据，提出一套提示设计框架。该框架包含八个功能组件，用于系统化定义、约束并适配机器人行为。定性分析揭示出机器人个性可读性差、需用户自适应调整，以及对安全、欺骗与治理的强烈伦理关切。基于此，本文提供配套的提示指南与概念验证模板，作为HRI研究中提示设计与报告的结构化支持工具。我们认为，提示设计应被视为一个社会技术问题，而非技术细节，必须明确能力边界、透明行为假设，并设置情境敏感的安全机制，以保障人机交互的可靠性与可解释性。
原文摘要 · Abstract (English)
Large language models (LLMs) are increasingly used for verbal interaction in social robots, yet prompt design in human-robot interaction (HRI) remains underspecified. As a result, robots may present hallucinated capabilities, unclear behavioural boundaries, and misleading personas. This paper develops a framework for prompt design in LLM-based robots and introduces a structured prompt template comprising eight functional components through which robot behaviour can be specified, bounded, and adapted. The framework is grounded in a review of prior LLM-based HRI work and complemented by survey and discussion data from HRI experts gathered at the Robo-Identity workshop at IEEE RO-MAN 2025 (N=27). The qualitative findings highlight limited legibility of robot personality, the need for user adaptation, and strong ethical concerns about safety, deception, and governance. Based on these findings, we present prompting guidelines accompanied by proof-of-concept template as a structured design and reporting aid for HRI research. We argue that prompt design should be treated as a socio-technical problem rather than a minor implementation detail, requiring explicit capability boundaries, transparent behavioural assumptions, and context-sensitive safeguards to support reliable and interpretable HRI.
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