@inproceedings{jin-etal-2024-implicit,
title = "Implicit Personalization in Language Models: A Systematic Study",
author = {Jin, Zhijing and
Heil, Nils and
Liu, Jiarui and
Dhuliawala, Shehzaad and
Qi, Yahang and
Sch{\"o}lkopf, Bernhard and
Mihalcea, Rada and
Sachan, Mrinmaya},
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.717",
pages = "12309--12325",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Implicit Personalization in Language Models: A Systematic Study
%A Jin, Zhijing
%A Heil, Nils
%A Liu, Jiarui
%A Dhuliawala, Shehzaad
%A Qi, Yahang
%A Schölkopf, Bernhard
%A Mihalcea, Rada
%A Sachan, Mrinmaya
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F jin-etal-2024-implicit
%X 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.
%U https://aclanthology.org/2024.findings-emnlp.717
%P 12309-12325
Markdown (Informal)
[Implicit Personalization in Language Models: A Systematic Study](https://aclanthology.org/2024.findings-emnlp.717) (Jin et al., Findings 2024)
ACL
- Zhijing Jin, Nils Heil, Jiarui Liu, Shehzaad Dhuliawala, Yahang Qi, Bernhard Schölkopf, Rada Mihalcea, and Mrinmaya Sachan. 2024. Implicit Personalization in Language Models: A Systematic Study. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 12309–12325, Miami, Florida, USA. Association for Computational Linguistics.