通过标准心理诊断实现确定性AI智能体人格表达
Deterministic AI Agent Personality Expression through Standard Psychological Diagnostics
AI总结：
该研究提出用成熟心理框架引导AI智能体实现确定性人格表达，验证了先进模型人格表达准确度更高，揭示其源于整体推理且微调不影响表达准确度，为人机交互优化和AI伦理设计提供支撑。
摘要：
由大语言模型驱动的人工智能（AI）系统在现代社会中日益普及，通过自然语言交互支撑了广泛的应用场景。随着AI智能体在日常生活中的普及，其通用且同质化的表达能力成为吸引力和推广应用的重大限制。人格表达是打造更具人性化、独特性的AI系统的关键前提。我们的研究表明，当使用成熟的心理学框架进行指令引导时，AI模型能够表达出确定且一致的人格，表达准确度因模型能力不同而存在差异。研究发现，GPT-4o、o1等更先进的模型在大五人格（Big Five）和迈尔斯-布里格斯（Myers-Briggs）两类评估中，表达指定人格的准确度最高；进一步分析表明，人格表达是智能与推理能力共同作用的结果。我们的结果显示，人格表达通过整体推理实现，而非逐题优化，且回答尺度指标的方差高于测试尺度指标。此外，我们发现模型微调对沟通风格的影响独立于人格表达准确度。这些发现为打造具备多样且稳定人格的AI智能体奠定了基础，可显著提升从教育到医疗等各类应用中的人机交互体验，同时还能催生更多更具独特性的AI智能体。这种对AI系统人格表达进行定量评估与落地的能力，为研究更具亲和力、可信度且符合伦理设计的AI开辟了新方向。
英文摘要：
Artificial intelligence (AI) systems powered by large language models have become increasingly prevalent in modern society, enabling a wide range of applications through natural language interaction. As AI agents proliferate in our daily lives, their generic and uniform expressiveness presents a significant limitation to their appeal and adoption. Personality expression represents a key prerequisite for creating more human-like and distinctive AI systems. We show that AI models can express deterministic and consistent personalities when instructed using established psychological frameworks, with varying degrees of accuracy depending on model capabilities. We find that more advanced models like GPT-4o and o1 demonstrate the highest accuracy in expressing specified personalities across both Big Five and Myers-Briggs assessments, and further analysis suggests that personality expression emerges from a combination of intelligence and reasoning capabilities. Our results reveal that personality expression operates through holistic reasoning rather than question-by-question optimization, with response-scale metrics showing higher variance than test-scale metrics. Furthermore, we find that model fine-tuning affects communication style independently of personality expression accuracy. These findings establish a foundation for creating AI agents with diverse and consistent personalities, which could significantly enhance human-AI interaction across applications from education to healthcare, while additionally enabling a broader range of more unique AI agents. The ability to quantitatively assess and implement personality expression in AI systems opens new avenues for research into more relatable, trustworthy, and ethically designed AI.
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。
