@inproceedings{ma-etal-2023-intersectional,
title = "Intersectional Stereotypes in Large Language Models: Dataset and Analysis",
author = "Ma, Weicheng and
Chiang, Brian and
Wu, Tong and
Wang, Lili and
Vosoughi, Soroush",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.575",
doi = "10.18653/v1/2023.findings-emnlp.575",
pages = "8589--8597",
abstract = "Despite many stereotypes targeting intersectional demographic groups, prior studies on stereotypes within Large Language Models (LLMs) primarily focus on broader, individual categories. This research bridges this gap by introducing a novel dataset of intersectional stereotypes, curated with the assistance of the ChatGPT model and manually validated. Moreover, this paper offers a comprehensive analysis of intersectional stereotype propagation in three contemporary LLMs by leveraging this dataset. The findings underscore the urgency of focusing on intersectional biases in ongoing efforts to reduce stereotype prevalence in LLMs.",
}
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<abstract>Despite many stereotypes targeting intersectional demographic groups, prior studies on stereotypes within Large Language Models (LLMs) primarily focus on broader, individual categories. This research bridges this gap by introducing a novel dataset of intersectional stereotypes, curated with the assistance of the ChatGPT model and manually validated. Moreover, this paper offers a comprehensive analysis of intersectional stereotype propagation in three contemporary LLMs by leveraging this dataset. The findings underscore the urgency of focusing on intersectional biases in ongoing efforts to reduce stereotype prevalence in LLMs.</abstract>
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%0 Conference Proceedings
%T Intersectional Stereotypes in Large Language Models: Dataset and Analysis
%A Ma, Weicheng
%A Chiang, Brian
%A Wu, Tong
%A Wang, Lili
%A Vosoughi, Soroush
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F ma-etal-2023-intersectional
%X Despite many stereotypes targeting intersectional demographic groups, prior studies on stereotypes within Large Language Models (LLMs) primarily focus on broader, individual categories. This research bridges this gap by introducing a novel dataset of intersectional stereotypes, curated with the assistance of the ChatGPT model and manually validated. Moreover, this paper offers a comprehensive analysis of intersectional stereotype propagation in three contemporary LLMs by leveraging this dataset. The findings underscore the urgency of focusing on intersectional biases in ongoing efforts to reduce stereotype prevalence in LLMs.
%R 10.18653/v1/2023.findings-emnlp.575
%U https://aclanthology.org/2023.findings-emnlp.575
%U https://doi.org/10.18653/v1/2023.findings-emnlp.575
%P 8589-8597
Markdown (Informal)
[Intersectional Stereotypes in Large Language Models: Dataset and Analysis](https://aclanthology.org/2023.findings-emnlp.575) (Ma et al., Findings 2023)
ACL