Online sexism appears in various forms, which makes its detection challenging. Although automated tools can enhance the identification of sexist content, they are often restricted to binary classification. Consequently, more subtle manifestations of sexism may remain undetected due to the lack of fine-grained, context-sensitive labels. To address this issue, we make the following contributions: (1) we present FineMuSe, a new multimodal sexism detection dataset in Spanish that includes both binary and fine-grained annotations; (2) we introduce a comprehensive hierarchical taxonomy that encompasses forms of sexism, non-sexism, and rhetorical devices of irony and humor; and (3) we evaluate a wide range of LLMs for both binary and fine-grained sexism detection. Our findings indicate that multimodal LLMs perform competitively with human annotators in identifying nuanced forms of sexism; however, they struggle to capture co-occurring sexist types when these are conveyed through visual cues.

翻译：网络性别歧视以多种形式出现，这使得其检测具有挑战性。尽管自动化工具可以增强对性别歧视内容的识别，但它们通常仅限于二元分类。因此，由于缺乏细粒度、上下文敏感的标签，更微妙的性别歧视表现形式可能无法被检测到。为解决这一问题，我们做出以下贡献：(1) 我们提出了FineMuSe，一个包含二元和细粒度标注的西班牙语多模态性别歧视检测新数据集；(2) 我们引入了一个全面的分层分类法，涵盖性别歧视形式、非性别歧视形式以及讽刺和幽默的修辞手法；(3) 我们评估了多种大语言模型在二元和细粒度性别歧视检测任务上的表现。我们的研究结果表明，多模态大语言模型在识别细微形式的性别歧视方面表现与人类标注者相当；然而，当这些性别歧视类型通过视觉线索传达时，模型难以捕捉同时出现的多种性别歧视类型。