让没用过聊天AI的人说话，才能造出真正有用的大模型
Attention to Non-Adopters
- 从不用ChatGPT的用户中挖掘真实需求，补全模型训练盲区
- 66%美国人从未用过ChatGPT，现有数据严重偏倚
- 适合关注公平性、人机交互与模型泛化的研究者
尽管基于语言模型的聊天系统日益普及，但截至2025年6月，仍有66%的美国人从未使用过ChatGPT。与此同时，大语言模型的研发与评估主要依赖采纳者数据（如日志、偏好数据），聚焦于特定地理、教育和性别背景下的有限群体。本文主张，纳入非采纳者视角对开发更具普适性和能力的LLM至关重要。我们指出，仅关注采纳者将导致忽视非采纳者重视的任务与需求，加剧受益不均，并造成模型开发与评估中的盲点。通过非采纳者的案例研究，我们展示了其需求如何不同于现有用户，如何引导发现新型推理任务，并提出通过以人为中心的方法系统整合非采纳者需求。
原文摘要 · Abstract (English)
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.
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