@inproceedings{zhou-etal-2026-attention,
    title = "Attention to Non-Adopters",
    author = "Zhou, Kaitlyn  and
      Gligori{\textbackslash}'c, Kristina  and
      Cheng, Myra  and
      Lam, Michelle S.  and
      Raman, Vyoma  and
      Aminu, Boluwatife  and
      Woo, Caeley  and
      Brockman, Michael  and
      Cha, Hannah  and
      Jurafsky, Dan",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.67/",
    pages = "1336--1366",
    ISBN = "979-8-89176-395-1",
    abstract = "Although language model{--}based chat systems are increasingly used in daily life, most Americans remain non-adopters of chat-based LLMs {---} as of June 2025, 66{\%} had never used ChatGPT. At the same time, LLM development and evaluation rely mainly on data from adopters (e.g., logs, preference data), focusing on the needs and tasks for a limited demographic group of adopters in terms of geographic location, education, and gender. In this position paper, we argue that incorporating \textit{non-adopter} perspectives is essential for developing broadly useful and capable LLMs. We contend that relying on methods that focus primarily on adopters will risk missing a range of tasks and needs prioritized by non-adopters, entrenching inequalities in who benefits from LLMs, and creating oversights in model development and evaluation. To illustrate this claim, we conduct case studies with non-adopters and show: how non-adopter needs diverge from those of current users, how non-adopter needs point us towards novel reasoning tasks, and how to systematically integrate non-adopter needs via human-centered methods."
}