@inproceedings{testa-etal-2026-gsi,
title = "{GSI}:detect - A Perspectivist Approach to Gender Stereotypes Identification in {I}talian",
author = "Testa, Davide and
Brenna, Sofia and
Speranza, Manuela and
Comandini, Gloria and
Cavagnoli, Stefania and
Magnini, Bernardo",
editor = "Dudy, Shiran and
Abercrombie, Gavin and
Basile, Valerio and
Leonardelli, Elisa and
Frenda, Simona",
booktitle = "Proceedings of the the fifth edition of {NLP}erspectives",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.nlperspectives-1.3/",
doi = "10.63317/373ps8r3346w",
pages = "21--32",
abstract = "The deconstruction of gender stereotypes is essential to prevent discrimination, marginalization and gender-based violence. Despite the increasing attention to this issue, research in this field often focuses on explicitly sexist or hateful communication, leaving out all the cases where stereotypes are produced unconsciously or even with apparently positive intentions. Moreover, the identification and analysis of gender stereotypes is often a very subjective task, heavily influenced by the researcher{'}s background, beliefs and personal sensitivity. In this context GSI:detect, a dataset for gender stereotypes identification in Italian, has been annotated following a perspectivist approach that gives value to the different points of view of four annotators. It has been designed to address (i) the lack of resources focusing on naturally occurring and non-hateful language conveying implicit or ambiguous forms of gender stereotypes, and (ii) the scarcity of datasets that can capture multiple interpretations as well as the inherent variation and disagreement in human perception. Baseline experiments with several LLMs confirm the challenging nature and value of such a linguistic resource, revealing both apparent differences and limitations in performance among the evaluated models, and raising questions about the extent to which current LLMs are suitable for detection and classification tasks in this field. Content warning: Examples taken from the GSI:detect dataset may contain sensitive or potentially distressing content."
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<abstract>The deconstruction of gender stereotypes is essential to prevent discrimination, marginalization and gender-based violence. Despite the increasing attention to this issue, research in this field often focuses on explicitly sexist or hateful communication, leaving out all the cases where stereotypes are produced unconsciously or even with apparently positive intentions. Moreover, the identification and analysis of gender stereotypes is often a very subjective task, heavily influenced by the researcher’s background, beliefs and personal sensitivity. In this context GSI:detect, a dataset for gender stereotypes identification in Italian, has been annotated following a perspectivist approach that gives value to the different points of view of four annotators. It has been designed to address (i) the lack of resources focusing on naturally occurring and non-hateful language conveying implicit or ambiguous forms of gender stereotypes, and (ii) the scarcity of datasets that can capture multiple interpretations as well as the inherent variation and disagreement in human perception. Baseline experiments with several LLMs confirm the challenging nature and value of such a linguistic resource, revealing both apparent differences and limitations in performance among the evaluated models, and raising questions about the extent to which current LLMs are suitable for detection and classification tasks in this field. Content warning: Examples taken from the GSI:detect dataset may contain sensitive or potentially distressing content.</abstract>
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%0 Conference Proceedings
%T GSI:detect - A Perspectivist Approach to Gender Stereotypes Identification in Italian
%A Testa, Davide
%A Brenna, Sofia
%A Speranza, Manuela
%A Comandini, Gloria
%A Cavagnoli, Stefania
%A Magnini, Bernardo
%Y Dudy, Shiran
%Y Abercrombie, Gavin
%Y Basile, Valerio
%Y Leonardelli, Elisa
%Y Frenda, Simona
%S Proceedings of the the fifth edition of NLPerspectives
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F testa-etal-2026-gsi
%X The deconstruction of gender stereotypes is essential to prevent discrimination, marginalization and gender-based violence. Despite the increasing attention to this issue, research in this field often focuses on explicitly sexist or hateful communication, leaving out all the cases where stereotypes are produced unconsciously or even with apparently positive intentions. Moreover, the identification and analysis of gender stereotypes is often a very subjective task, heavily influenced by the researcher’s background, beliefs and personal sensitivity. In this context GSI:detect, a dataset for gender stereotypes identification in Italian, has been annotated following a perspectivist approach that gives value to the different points of view of four annotators. It has been designed to address (i) the lack of resources focusing on naturally occurring and non-hateful language conveying implicit or ambiguous forms of gender stereotypes, and (ii) the scarcity of datasets that can capture multiple interpretations as well as the inherent variation and disagreement in human perception. Baseline experiments with several LLMs confirm the challenging nature and value of such a linguistic resource, revealing both apparent differences and limitations in performance among the evaluated models, and raising questions about the extent to which current LLMs are suitable for detection and classification tasks in this field. Content warning: Examples taken from the GSI:detect dataset may contain sensitive or potentially distressing content.
%R 10.63317/373ps8r3346w
%U https://aclanthology.org/2026.nlperspectives-1.3/
%U https://doi.org/10.63317/373ps8r3346w
%P 21-32
Markdown (Informal)
[GSI:detect - A Perspectivist Approach to Gender Stereotypes Identification in Italian](https://aclanthology.org/2026.nlperspectives-1.3/) (Testa et al., NLPerspectives 2026)
ACL