@inproceedings{rizvi-etal-2025-autalic,
    title = "{AUTALIC}: A Dataset for Anti-{AUT}istic Ableist Language In Context",
    author = "Rizvi, Naba  and
      Strickland, Harper  and
      Gitelman, Daniel  and
      Flores, Alexis Morales  and
      Cooper, Tristan  and
      Kallepalli, Aekta  and
      Alurkar, Akshat  and
      Owens, Haaset  and
      Ahmedi, Saleha  and
      Khirwadkar, Isha  and
      Munyaka, Imani N. S.  and
      Ousidhoum, Nedjma",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1022/",
    pages = "20999--21015",
    ISBN = "979-8-89176-251-0",
    abstract = "As our awareness of autism and ableism continues to increase, so does our understanding of ableist language towards autistic people. Such language poses a significant challenge in NLP research due to its subtle and context-dependent nature. Yet, detecting anti-autistic ableist language remains underexplored, with existing NLP tools often failing to capture its nuanced expressions. We present AUTALIC, the first dataset dedicated to the detection of anti-autistic ableist language in context, addressing a significant gap in the field. AUTALIC comprises 2,400 autism-related sentences collected from Reddit, accompanied by surrounding context, and annotated by trained experts with backgrounds in neurodiversity. Our comprehensive evaluation reveals that current language models, including state-of-the-art LLMs, struggle to reliably identify anti-autistic ableism and diverge from human judgments, underscoring their limitations in this domain. We publicly release our dataset along with the individual annotations, providing an essential resource for developing more inclusive and context-aware NLP systems that better reflect diverse perspectives."
}