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LLMs aid sexism detection in memes and videos

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a system for identifying and characterizing sexism in multimodal content like memes and short-form videos. Their approach combines visual, textual, and LLM-derived semantic features, feeding them into gradient-boosted regression models. The study found that LLM-derived cues significantly improved sexism identification in memes, while video analysis proved sensitive to feature selection and cross-modal noise, suggesting a need for more robust temporal modeling in video content. AI

IMPACT This research demonstrates the utility of LLMs in identifying nuanced harmful content, potentially improving AI safety tools for content moderation.

RANK_REASON The cluster contains a research paper detailing a new methodology for multimodal sexism identification.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LLMs aid sexism detection in memes and videos

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The cluster contains a research paper detailing a new methodology for multimodal sexism identification.
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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Kyriakos Chaviaras, Maria Lymperaiou, Athanasios Voulodimos ·

    Multimodal Sexism Identification and Characterization using Large Language Models and Gradient Boosting

    arXiv:2606.05997v1 Announce Type: new Abstract: We present the AILS-NTUA submission to the EXIST 2026 Lab at CLEF, addressing multimodal sexism identification and characterization in memes (Task 2) and short-form videos (Task 3). Our system follows a feature-engineered late-fusio…

  2. arXiv cs.CV TIER_1 English(EN) · Athanasios Voulodimos ·

    Multimodal Sexism Identification and Characterization using Large Language Models and Gradient Boosting

    We present the AILS-NTUA submission to the EXIST 2026 Lab at CLEF, addressing multimodal sexism identification and characterization in memes (Task 2) and short-form videos (Task 3). Our system follows a feature-engineered late-fusion pipeline built around gradient-boosted regress…

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