用心理学量表给AI Agent赋予人格，让其决策更像真人。
Designing AI-Agents with Personalities: A Psychometric Approach
- 基于五大性格特质框架，用BFI-2量表设计提示词生成人格化AI。
- 使用扩展版BFI-2提示的AI在道德与冒险测试中最接近人类行为模式。
- 适合心理学预研，但精细差异仍需真人参与，不可替代高精度研究。
我们提出一种基于五大性格框架的可量化人格赋值方法，用于AI-Agent。三项研究验证其可行性：研究1表明大语言模型能捕捉五大性格量表间的语义相似性；研究2通过BFI-2不同格式提示生成AI-Agent，发现新模型在迷你标记测试中更贴近人类反应，但因子载荷模式存在不一致；研究3在风险决策与道德困境情景中验证，采用BFI-2-Expanded格式提示的模型最贴近人类人格-决策关联，而安全对齐模型普遍高估“道德”评分。结果表明，AI-Agent在性格输入与输出响应的相关性上与人类一致，适合作为初步研究工具；但细微反应模式差异说明其尚无法完全替代人类参与精密或高风险项目。
原文摘要 · Abstract (English)
We introduce a methodology for assigning quantifiable and psychometrically validated personalities to AI-Agents using the Big Five framework. Across three studies, we evaluate its feasibility and limitations. In Study 1, we show that large language models (LLMs) capture semantic similarities among Big Five measures, providing a basis for personality assignment. In Study 2, we create AI-Agents using prompts designed based on the Big Five Inventory-2 (BFI-2) in different format, and find that AI-Agents powered by new models align more closely with human responses on the Mini-Markers test, although the finer pattern of results (e.g., factor loading patterns) were sometimes inconsistent. In Study 3, we validate our AI-Agents on risk-taking and moral dilemma vignettes, finding that models prompted with the BFI-2-Expanded format most closely reproduce human personality-decision associations, while safety-aligned models generally inflate 'moral' ratings. Overall, our results show that AI-Agents align with humans in correlations between input Big Five traits and output responses and may serve as useful tools for preliminary research. Nevertheless, discrepancies in finer response patterns indicate that AI-Agents cannot (yet) fully substitute for human participants in precision or high-stakes projects.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。