标题： 对非采用者的注意 显示英文标题

标题： Attention to Non-Adopters

摘要： 尽管基于语言模型的聊天系统在日常生活中越来越被使用，但截至2025年6月，仍有66%的美国人从未使用过ChatGPT。 同时，LLM的开发和评估主要依赖于采用者的数据（例如，日志、偏好数据），在地理区域、教育程度和性别等方面，关注的是有限人口群体的需求和任务。 在本文中，我们认为将非采用者的观点纳入考虑对于开发广泛有用且功能强大的LLM是至关重要的。 我们认为，仅依赖主要针对采用者的方法可能会导致错过非采用者优先考虑的各种任务和需求，加剧谁从LLM中受益的不平等现象，并在模型开发和评估中造成遗漏。 为了说明这一观点，我们与非采用者进行了案例研究，并展示了：非采用者的需求如何与现有用户的需求不同，非采用者的需求如何引导我们发现新的推理任务，以及如何通过以人类为中心的方法系统地整合非采用者的需求。 显示英文摘要

摘要： 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.