@inproceedings{tint-2025-guardrails,
    title = "Guardrails, not Guidance: Understanding Responses to {LGBTQ}+ Language in Large Language Models",
    author = "Tint, Joshua",
    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.2/",
    pages = "6--16",
    ISBN = "979-8-89176-244-2",
    abstract = "Language models have integrated themselves into many aspects of digital life, shaping everything from social media to translation. This paper investigates how large language models (LLMs) respond to LGBTQ+ slang and heteronormative language. Through two experiments, the study assesses the emotional content and the impact of queer slang on responses from models including GPT-3.5, GPT-4o, Llama2, Llama3, Gemma and Mistral. The findings reveal that heteronormative prompts can trigger safety mechanisms, leading to neutral or corrective responses, while LGBTQ+ slang elicits more negative emotions. These insights punctuate the need to provide equitable outcomes for minority slangs and argots, in addition to eliminating explicit bigotry from language models."
}