@inproceedings{bandyopadhyay-etal-2026-saji,
    title = "{SAJI}{\_}{E}nglish@{LT}-{EDI} 2026: Detection of Homophobia and Transphobia in {I}nternet Memes Using Zero-Shot Learning",
    author = "Bandyopadhyay, Jishnu  and
      Kushwaha, Saloni  and
      Sharma, Deepawali  and
      Singh, Aakash",
    editor = "Chakravarthi, Bharathi Raja  and
      B, Bharathi  and
      Buitelaar, Paul  and
      Thenmozhi, Durairaj  and
      Garc{\'i}a Cumbreras, Miguel {\'A}ngel  and
      Jim{\'e}nez Zafra, Salud Mar{\'i}a",
    booktitle = "Proceedings of the Sixth Workshop on Language Technology for Equality, Diversity, Inclusion",
    month = jul,
    year = "2026",
    address = "Virtual (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.ltedi-1.26/",
    pages = "217--221",
    ISBN = "979-8-89176-424-8",
    abstract = "Social media is now an important platform for communication and interaction. At the same time, the amount of abusive and harmful content online has also increased. Offensive language and hate speech are making these platforms less safe and less welcoming for users. Many of these contents include homophobic and transphobic remarks aimed at the LGBT+ community. Such behaviour damages healthy discussions and can negatively affect users. For this reason, it is important to detect these contents early so they can be flagged and removed to maintain a healthy online well-being. The issue becomes more difficult when harmful messages appear in popular formats like memes. Memes are widely used by younger users to communicate online. Because they combine images and text, detecting offensive meaning becomes challenging. In this work, we attempt to address this problem. We develop a method to identify such content using the meme dataset released for the LT-EDI 2026 challenge and secured rank 5 in the shared task. We propose a Zero-shot learning based method employing two LLMs (Qwen2.5-VL-3B-Instruct and Meta-Llama-3-8B-Instruct) to generate descriptions and classify such memes. We achieved a macro F1-score of 0.55 for the English language meme."
}