通过心理学支架合理化改进语言模型人格设定
Improving Language Model Personas via Rationalization with Psychological Scaffolds
AI总结：
针对现有语言模型人格构建未挖掘用户判断底层推理的问题，提出PB&J框架，借助心理学支架生成理论锚定的用户行为理由，在偏好预测任务上表现优于基线且媲美人工理由方案。
摘要：
通过用户描述或人格提示引导的语言模型正被用于预测用户的偏好与观点。然而，现有人格构建方法大多依赖用户的人口统计属性和/或先前判断，却未涉及用户判断背后的任何底层推理机制。我们提出PB&J（行为与判断心理学）框架，通过纳入用户做出特定判断的潜在理由来优化语言模型人格设定。我们的理由由语言模型生成，可基于用户的经历、人格特质或信念对其行为进行显式推理。该方法采用心理学支架：即大五人格特质、原始世界信念等结构化框架，帮助将生成的理由锚定在现有理论之上。在公众舆论与电影偏好预测任务上的实验表明，增强了PB&J理由的语言模型人格设定，其表现始终优于仅以用户人口统计信息和/或判断为条件的人格设定——包括使用模型默认思维链（未锚定心理学理论）的设定。此外，我们的PB&J人格设定表现与使用人工撰写理由的人格设定相当，彰显了由现有理论引导的合成理由的潜力。
英文摘要：
Language models prompted with a user description or persona are being used to predict the user's preferences and opinions. However, existing approaches to building personas mostly rely on a user's demographic attributes and/or prior judgments, but not on any underlying reasoning behind a user's judgments. We introduce PB&J (Psychology of Behavior and Judgments), a framework that improves LM personas by incorporating potential rationales for why the user could have made a certain judgment. Our rationales are generated by a language model to explicitly reason about a user's behavior on the basis of their experiences, personality traits, or beliefs. Our method employs psychological scaffolds: structured frameworks such as the Big 5 Personality Traits or Primal World Beliefs to help ground the generated rationales in existing theories. Experiments on public opinion and movie preference prediction tasks demonstrate that language model personas augmented with PB&J rationales consistently outperform personas conditioned only on user demographics and / or judgments, including those that use a model's default chain-of-thought, which is not grounded in psychological theories. Additionally, our PB&J personas perform competitively with those using human-written rationales, suggesting the potential of synthetic rationales guided by existing theories.
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。
