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美国大型语言模型意识,使用和感知的差距:来自全国代表性的纵向调查的证据

Marco Angrisani¹, Maria Casanova², Nathanael J Fast³

¹Center for Economic and Social Research and Economics Department, University of Southern California, 635 Downey Way, Los Angeles, CA 90089, USA.

|February 16, 2026

概括

大型语言模型 (LLM) 的认识和使用在美国人口统计学中显示出显著的差异. 采用差距持续并扩大,与访问和个体特征相关,影响公平的AI整合.

科学领域:

人工智能的人工智能
技术社会学技术的社会学.
人与计算机的交互

背景情况:

大型语言模型 (LLM) 为生活的各个方面提供了变革的潜力.
了解LLM的不同采用模式对于公平的技术部署至关重要.
之前的研究还没有完全捕捉到LLM意识和使用在不同人群中的不断变化的景观.

研究的目的:

调查美国成年人对LLM的认识,使用和看法.
确定不同收养率的社会人口群体.
在一年的时间内跟踪LLM采用的变化.

主要方法:

对12224名美国居民的全国代表性在线小组进行了两波调查.
数据收集持续了一年,捕捉了LLM采用的时间趋势.
统计分析检查了社会人口统计因素与LLM使用之间的相关性.

主要成果:

观察到显著的使用差距,男性,年轻人,高收入人士和STEM职业人士的采用率更高.
具有较高认知能力,互联网素养和对经验开放的受访者更有可能使用LLMs.
在LLM采用这些差异没有下降,似乎在研究期间扩大.

结论:

法学士的采用差异与与获得相关的因素 (收入,职业,数字技能) 和个人特征 (开放性,政治取向) 相关.
这些发现凸显了LLM在美国人口中的动态和不均的整合.
解决这些采用差距对于确保人工智能技术带来广泛和公平的好处至关重要.

关键词:

在法律上,LLMs.数字的数字划分.生成型的人工智能不平等不平等不平等不平等技术采用技术采用

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