| Téléchargement | - Voir la version finale : What makes a good counter-stereotype? Evaluating strategies for automated responses to stereotypical text (PDF, 907 Kio)
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| DOI | Trouver le DOI : https://doi.org/10.18653/v1/2023.sicon-1.4 |
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| Auteur | Rechercher : Fraser, Kathleen C.1; Rechercher : Kiritchenko, Svetlana1; Rechercher : Nejadgholi, Isar1; Rechercher : Kerkhof, Anna |
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| Affiliation | - Conseil national de recherches du Canada. Technologies numériques
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| Format | Texte, Article |
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| Conférence | First Workshop on Social Influence in Conversations (SICon 2023), July 14, 2023, Toronto, Ontario |
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| Résumé | When harmful social stereotypes are expressed on a public platform, they must be addressed in a way that educates and informs both the original poster and other readers, without causing offence or perpetuating new stereotypes. In this paper, we synthesize findings from psychology and computer science to propose a set of potential counter-stereotype strategies. We then automatically generate such counter-stereotypes using ChatGPT, and analyze their correctness and expected effectiveness at reducing stereotypical associations. We identify the strategies of denouncing stereotypes, warning of consequences, and using an empathetic tone as three promising strategies to be further tested. |
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| Date de publication | 2023-07-14 |
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| Maison d’édition | Association for Computational Linguistics |
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| Licence | |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 0b1f722f-e170-4108-8347-ed43d325239c |
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| Enregistrement créé | 2023-07-19 |
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| Enregistrement modifié | 2023-11-01 |
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