@inproceedings{zhou-etal-2026-attention,
title = "Attention to Non-Adopters",
author = "Zhou, Kaitlyn and
Gligori{\'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://aclanthology.org/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."
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<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 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.</abstract>
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%0 Conference Proceedings
%T Attention to Non-Adopters
%A Zhou, Kaitlyn
%A Gligorić, Kristina
%A Cheng, Myra
%A Lam, Michelle S.
%A Raman, Vyoma
%A Aminu, Boluwatife
%A Woo, Caeley
%A Brockman, Michael
%A Cha, Hannah
%A Jurafsky, Dan
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F zhou-etal-2026-attention
%X 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 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.
%U https://aclanthology.org/2026.findings-acl.67/
%P 1336-1366
Markdown (Informal)
[Attention to Non-Adopters](https://aclanthology.org/2026.findings-acl.67/) (Zhou et al., Findings 2026)
ACL
- Kaitlyn Zhou, Kristina Gligorić, Myra Cheng, Michelle S. Lam, Vyoma Raman, Boluwatife Aminu, Caeley Woo, Michael Brockman, Hannah Cha, and Dan Jurafsky. 2026. Attention to Non-Adopters. In Findings of the Association for Computational Linguistics: ACL 2026, pages 1336–1366, San Diego, California, United States. Association for Computational Linguistics.