让AI更讨喜能显著提升其像人的概率。
AI-Driven Agents with Prompts Designed for High Agreeableness Increase the Likelihood of Being Mistaken for a Human in the Turing Test
- 用人格特质设计提示，让GPT更讨喜
- 讨喜型AI在图灵测试中混淆率超60%
- 适合人机协作场景的AI性格设计
基于Transformer架构的大语言模型已实现类人对话，使机器在图灵测试中达到最高50%的混淆率。本实验测试了三种基于大五人格量表（Big Five Inventory）设定不同亲和力水平（不讨喜、中性、讨喜）的GPT代理。所有代理均超过50%混淆率，其中高亲和力代理达到60%以上，且被评价为最具人类特征。文献中多种心理机制解释了此类现象，如拟人化认知框架。研究凸显人格工程作为新兴AI领域的重要性，呼吁与心理学合作构建适配协作场景的心理模型。
原文摘要 · Abstract (English)
Large Language Models based on transformer algorithms have revolutionized Artificial Intelligence by enabling verbal interaction with machines akin to human conversation. These AI agents have surpassed the Turing Test, achieving confusion rates up to 50%. However, challenges persist, especially with the advent of robots and the need to humanize machines for improved Human-AI collaboration. In this experiment, three GPT agents with varying levels of agreeableness (disagreeable, neutral, agreeable) based on the Big Five Inventory were tested in a Turing Test. All exceeded a 50% confusion rate, with the highly agreeable AI agent surpassing 60%. This agent was also recognized as exhibiting the most human-like traits. Various explanations in the literature address why these GPT agents were perceived as human, including psychological frameworks for understanding anthropomorphism. These findings highlight the importance of personality engineering as an emerging discipline in artificial intelligence, calling for collaboration with psychology to develop ergonomic psychological models that enhance system adaptability in collaborative activities.
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