@inproceedings{sommerauer-etal-2025-simulating,
title = "Simulating Identity, Propagating Bias: Abstraction and Stereotypes in {LLM}-Generated Text",
author = "Sommerauer, Pia and
Rambelli, Giulia and
Caselli, Tommaso",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.1080/",
doi = "10.18653/v1/2025.findings-emnlp.1080",
pages = "19812--19831",
ISBN = "979-8-89176-335-7",
abstract = "Persona-prompting is a growing strategy to steer LLMs toward simulating particular perspectives or linguistic styles through the lens of a specified identity. While this method is often used to personalize outputs, its impact on how LLMs represent social groups remains underexplored. In this paper, we investigate whether persona-prompting leads to different levels of linguistic abstraction{---}an established marker of stereotyping{---}when generating short texts linking socio-demographic categories with stereotypical or non-stereotypical attributes. Drawing on the Linguistic Expectancy Bias framework, we analyze outputs from six open-weight LLMs under three prompting conditions, comparing 11 persona-driven responses to those of a generic AI assistant. To support this analysis, we introduce Self-Stereo, a new dataset of self-reported stereotypes from Reddit. We measure abstraction through three metrics: concreteness, specificity, and negation. Our results highlight the limits of persona-prompting in modulating abstraction in language, confirming criticisms about the ecology of personas as representative of socio-demographic groups and raising concerns about the risk of propagating stereotypes even when seemingly evoking the voice of a marginalized groups."
}Markdown (Informal)
[Simulating Identity, Propagating Bias: Abstraction and Stereotypes in LLM-Generated Text](https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.1080/) (Sommerauer et al., Findings 2025)
ACL