Do Large Language Models understand mansplaining? Well, actually...

Perez Almendros, Carla ORCID: https://orcid.org/0000-0001-9360-4011 and Camacho Collados, Jose ORCID: https://orcid.org/0000-0003-1618-7239 2024. Do Large Language Models understand mansplaining? Well, actually... Presented at: LREC-COLING 2024 - The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, Torino, Itay, 20-25 May 2024. Published in: Calzolari, Nicoletta, Kan, Min-Yen, Hoste, Veronique, Lenci, Alessandro, Sakti, Sakrani and Xue, Nianwen eds. Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). ELRA and ICCL, pp. 5235-5246.

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Abstract

Gender bias has been widely studied by the NLP community. However, other more subtle variations of it, such as mansplaining, have yet received little attention. Mansplaining is a discriminatory behaviour that consists of a condescending treatment or discourse towards women. In this paper, we introduce and analyze Well, actually..., a corpus of 886 mansplaining stories experienced by women. We analyze the corpus in terms of features such as offensiveness, sentiment or misogyny, among others. We also explore to what extent Large Language Models (LLMs) can understand and identify mansplaining and other gender-related microaggressions. Specifically, we experiment with ChatGPT-3.5-Turbo and LLaMA-2 (13b and 70b), with both targeted and open questions. Our findings suggest that, although they can identify mansplaining to some extent, LLMs still struggle to point out this attitude and will even reproduce some of the social patterns behind mansplaining situations, for instance by praising men for giving unsolicited advice to women.

Item Type:	Conference or Workshop Item (Paper)
Status:	Published
Schools:	Schools > Computer Science & Informatics
Publisher:	ELRA and ICCL
ISBN:	9782493814104
Date of First Compliant Deposit:	20 February 2025
Last Modified:	20 Feb 2025 09:45
URI:	https://orca.cardiff.ac.uk/id/eprint/175713

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