Implicit Personalization in Language Models: A Systematic Study (2024)

Abstract: 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.

## introduction let's begin with a brain teaser: what color is a football? as illustrated in figure 1 , we first infer from the spelling "color" -as opposed to "colour" -that the user speaks american english. therefore, we answer "brown," in contrast to the black and white pattern typically for a football in british english. inspired by this example, we propose the concept of implicit personalization (ip). grounded in a structural causal model (scm; @xcite , we define ip as a process that first infers a user's background from the way a question is posed, and then tailors the response to fit this background, as in figure 1 . while many studies have separately explored different aspects of this problem semantics s ## overview of three case studies as highlighted before, although the mathematical formulation in section 2 describes the syntax of the ip problem (so that we can answer "does ip exist?"), the subsequent moral reasoning of ip (to answer "is ip good?") requires the semantics of it, namely what exact value the application scenario a and type of the background b take. in this section, we introduce three meticulously designed case studies with the goal of introducing the diversity behind this problem. ## moving forward based on the framework and findings in our study, we propose several suggestions for the community. future development workflow. we visualize a suggested workflow for future ip development in llms in figure 3 . using the standard flowchart notation @xcite , we suggest actions based on two questions: (1) whether ip exists in the llm (using our math framework in section 2), and given an llm, application scenario a, and the type of the user background b discuss the ethical implications of ip start is ip good in this case? ip detection does ip exist in this llm? ip is good and exists, or ip is bad and is absent done improve model awareness of ip ip is good, but is absent remove ip ip is bad, but exists done done possibility 1 possibility 2 possibility 3 (sec 2, 5-7) (sec 3) pose the concept of value alignment for ip, which holds if ip is ethical and exists, or if ip is unethical and also does not exist (i.e., "possibility 2" in figure 3 ). however, a model is misaligned if an ethically desired ip is missing (i.e., "possibility 1"), or an unethical ip is present (i.e., "possibility 3"). for possibility 1, we suggest future work improve model awareness to ip. the scientific question behind the ip improvement is whether models already have the capability but just lack the right prompt to induce it, or whether further training is needed. for possibility 3, future work could explore different methods like post-processing prompts for user identity obfuscation or transferring to a default style. another approach relies on advancements in llm interpretability research to eliminate the model's ability for user identity inference @xmath0, making it "blind" towards the implicitly-revealed user background. a community-wide benchmark. our case studies reveal the importance of different instantiations of the ip problem. we encourage the community to initiate a joint benchmark, ip-bench, to gather and publish different test cases. learning from successful examples such as big-bench @xcite and natural instructions @xcite , we can also open-source ip-bench to welcome new datasets and application-specific setups. standard practice in the ethics section. as discussed in section 3, the ethical implications of ip require examination from multiple perspectives due to the potential for dual use. thus, we recommend all future work to include a detailed ethics section to address the questions listed in section 3.2. ## conclusion in conclusion, we presented a systematic study on implicit personalization (ip) in llms, from a mathematical formulation based on scms and hypothesis testing, to the moral reasoning principles. we instantiated our framework with three diverse case studies demonstrating different ethical implications and novel operationalization techniques. lastly, we presented a list of suggestions to mitigate the ethical problems of ip and encourage community-wide actions. our work lays a solid theoretical foundation for studying ip and paves a way for responsible development of llms that account for ip.