Implicit Personalization (IP) is a phenomenon of language models inferring a user's background from the implicit cues in the input prompts and tailoring the response based on this inference. While previous work has touched upon various instances of this problem, there lacks a unified framework to study this behavior. This work systematically studies IP through a rigorous mathematical formulation, a multi-perspective moral reasoning framework, and a set of case studies. Our theoretical foundation for IP relies on a structural causal model and introduces a novel method, indirect intervention, to estimate the causal effect of a mediator variable that cannot be directly intervened upon. Beyond the technical approach, we also introduce a set of moral reasoning principles based on three schools of moral philosophy to study when IP may or may not be ethically appropriate. Equipped with both mathematical and ethical insights, we present three diverse case studies illustrating the varied nature of the IP problem and offer recommendations for future research. Our code is at https://github.com/jiarui-liu/IP, and our data is at https://huggingface.co/datasets/Jerry999/ImplicitPersonalizationData.

翻译：隐式个性化（IP）是指语言模型从输入提示的隐含线索中推断用户背景，并基于此推断定制回应的现象。尽管先前研究已涉及该问题的多种实例，但尚缺乏研究此类行为的统一框架。本研究通过严谨的数学形式化、多视角道德推理框架及系列案例研究，对IP进行了系统性探究。我们为IP建立的理论基础依托于结构因果模型，并引入了一种新颖的间接干预方法，用于估计无法直接干预的中介变量的因果效应。除技术方法外，我们还基于三大道德哲学流派提出了一套道德推理原则，以探究IP在何种情境下可能符合或不符合伦理规范。结合数学与伦理学视角，我们呈现了三个多元化的案例研究，阐明了IP问题的多样性特征，并为未来研究提出了建议。代码发布于https://github.com/jiarui-liu/IP，数据集位于https://huggingface.co/datasets/Jerry999/ImplicitPersonalizationData。