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Implicit Personalization in Language Models: A Systematic Study

Zhijing Jin, Nils Heil, Jiarui Liu, Shehzaad Dhuliawala, Yahang Qi, Bernhard Schölkopf, Rada Mihalcea, Mrinmaya Sachan

TL;DR

This work systematically studies IP through a rigorous mathematical formulation, a multi-perspective moral reasoning framework, and a set of case studies, and introduces a novel method, indirect intervention, to estimate the causal effect of a mediator variable that cannot be directly intervened upon.

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. Our code is at https://github.com/jiarui-liu/IP, and our data is at https://huggingface.co/datasets/Jerry999/ImplicitPersonalizationData.

Implicit Personalization in Language Models: A Systematic Study

TL;DR

This work systematically studies IP through a rigorous mathematical formulation, a multi-perspective moral reasoning framework, and a set of case studies, and introduces a novel method, indirect intervention, to estimate the causal effect of a mediator variable that cannot be directly intervened upon.

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. Our code is at https://github.com/jiarui-liu/IP, and our data is at https://huggingface.co/datasets/Jerry999/ImplicitPersonalizationData.
Paper Structure (78 sections, 6 equations, 8 figures, 14 tables)

This paper contains 78 sections, 6 equations, 8 figures, 14 tables.

Table of Contents

  1. Introduction
  2. A Mathematical Framework for IP
  3. Causal Graph Formulation
  4. Problem Statement
  5. Quantifying the Interventional Effect: Moving from Numerical Changes to Text Changes
  6. Sample Generation via Indirect Intervention
  7. Single Hypothesis Testing Methods
  8. Permutation-based Method for Interval Responses
  9. Sign-Test for Non-Interval Responses
  10. Multiple Hypothesis Testing
  11. A Moral Reasoning Framework for IP
  12. The Moral Question behind IP
  13. Principles to Reason about the Ethicality of IP (for Human Designers)
  14. Overview of Three Case Studies
  15. Desiderata of the Case Design
  16. ...and 63 more sections

Figures (8)

  • Figure 1: Overview of the general formulation of IP, where the model infers the user background from the text input, and then customizes the response.
  • Figure 2: Sample generation via indirect intervention.
  • Figure 3: A flowchart for future IP development.
  • Figure 4: Generation of topics related to the given domain.
  • Figure 5: Generation of factual questions related to the given topic.
  • ...and 3 more figures

Theorems & Definitions (1)

  • Definition 1