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ACL 2025longmain

AUTALIC: A Dataset for Anti-AUTistic Ableist Language In Context

Naba Rizvi, Harper Strickland, Daniel Gitelman, Alexis Morales Flores, Tristan Cooper, Aekta Kallepalli, Akshat Alurkar, Haaset Owens, Saleha Ahmedi, Isha Khirwadkar, Imani N. S. Munyaka, Nedjma Ousidhoum

Computer Science and Engineering Department, University of California, San Diego · Cardiff University

PDF 由论文原始站点提供,PaperCompass 不保存论文文件。DOI 10.18653/v1/2025.acl-long.1022 ↗

摘要

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.