@inproceedings{weeber-etal-2026-one,
title = "One Persona, Many Cues, Different Results: How Sociodemographic Cues Impact {LLM} Personalization",
author = "Weeber, Franziska and
Neplenbroek, Vera and
Batzner, Jan and
Pad{\'o}, Sebastian",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.2079/",
pages = "44892--44921",
ISBN = "979-8-89176-390-6",
abstract = "Personalization of LLMs by sociodemographic subgroup often improves user experience, but can also introduce or amplify biases and unfairoutcomes across groups. Prior work has employed so-called personas, sociodemographic user attributes conveyed to a model, to studybias in LLMs by relying on a single cue to prompt a persona, such as user names or explicit attribute mentions. This disregards LLM sensitivity to prompt variation and the rarity of some cues in real interactions (external validity). We compare six commonly used personacues across seven open and proprietary LLMs on four writing and advice tasks. While cues are overall highly correlated, they produce sub-stantial variance in responses across personas that can change findings on persona-induced differences and bias. We therefore cautionagainst claims based on single persona cues, especially when they are overly explicit and have low external validity."
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<abstract>Personalization of LLMs by sociodemographic subgroup often improves user experience, but can also introduce or amplify biases and unfairoutcomes across groups. Prior work has employed so-called personas, sociodemographic user attributes conveyed to a model, to studybias in LLMs by relying on a single cue to prompt a persona, such as user names or explicit attribute mentions. This disregards LLM sensitivity to prompt variation and the rarity of some cues in real interactions (external validity). We compare six commonly used personacues across seven open and proprietary LLMs on four writing and advice tasks. While cues are overall highly correlated, they produce sub-stantial variance in responses across personas that can change findings on persona-induced differences and bias. We therefore cautionagainst claims based on single persona cues, especially when they are overly explicit and have low external validity.</abstract>
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%0 Conference Proceedings
%T One Persona, Many Cues, Different Results: How Sociodemographic Cues Impact LLM Personalization
%A Weeber, Franziska
%A Neplenbroek, Vera
%A Batzner, Jan
%A Padó, Sebastian
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F weeber-etal-2026-one
%X Personalization of LLMs by sociodemographic subgroup often improves user experience, but can also introduce or amplify biases and unfairoutcomes across groups. Prior work has employed so-called personas, sociodemographic user attributes conveyed to a model, to studybias in LLMs by relying on a single cue to prompt a persona, such as user names or explicit attribute mentions. This disregards LLM sensitivity to prompt variation and the rarity of some cues in real interactions (external validity). We compare six commonly used personacues across seven open and proprietary LLMs on four writing and advice tasks. While cues are overall highly correlated, they produce sub-stantial variance in responses across personas that can change findings on persona-induced differences and bias. We therefore cautionagainst claims based on single persona cues, especially when they are overly explicit and have low external validity.
%U https://aclanthology.org/2026.acl-long.2079/
%P 44892-44921
Markdown (Informal)
[One Persona, Many Cues, Different Results: How Sociodemographic Cues Impact LLM Personalization](https://aclanthology.org/2026.acl-long.2079/) (Weeber et al., ACL 2026)
ACL