@inproceedings{nguyen-etal-2025-leveraging,
    title = "Leveraging Large Language Models in Detecting Anti-{LGBTQIA}+ User-generated Texts",
    author = "Nguyen, Quoc-Toan  and
      Nguyen, Josh  and
      Pham, Tuan  and
      Teahan, William John",
    editor = "Pranav, A  and
      Valentine, Alissa  and
      Bhatt, Shaily  and
      Long, Yanan  and
      Subramonian, Arjun  and
      Bertsch, Amanda  and
      Lauscher, Anne  and
      Gupta, Ankush",
    booktitle = "Proceedings of the Queer in AI Workshop",
    month = may,
    year = "2025",
    address = "Hybrid format (in-person and virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.queerinai-main.4/",
    pages = "26--34",
    ISBN = "979-8-89176-244-2",
    abstract = "Anti-LGBTQIA+ texts in user-generated content pose significant risks to online safety and inclusivity. This study investigates the capabilities and limitations of five widely adopted Large Language Models (LLMs){---}DeepSeek-V3, GPT-4o, GPT-4o-mini, GPT-o1-mini, and Llama3.3-70B{---}in detecting such harmful content. Our findings reveal that while LLMs demonstrate potential in identifying offensive language, their effectiveness varies across models and metrics, with notable shortcomings in calibration. Furthermore, linguistic analysis exposes deeply embedded patterns of discrimination, reinforcing the urgency for improved detection mechanisms for this marginalised population. In summary, this study demonstrates the significant potential of LLMs for practical application in detecting anti-LGBTQIA+ user-generated texts and provides valuable insights from text analysis that can inform topic modelling. These findings contribute to developing safer digital platforms and enhancing protection for LGBTQIA+ individuals."
}