SES影响人与AI语言模型的互动方式，高低收入群体表现差异明显。
The AI Gap: How Socioeconomic Status Affects Language Technology Interactions
- 调研1000人并分析6482条真实对话，发现不同社会经济背景用户使用习惯不同。
- 高SES人群更抽象、简洁，关注包容性与旅行；低SES人群更拟人化，用语更具体。
- 研究揭示技术普及背后的社会差距，提醒开发者考虑多元语言需求。
社会经济地位（SES）从根本上影响人际互动，也影响人们对数字技术如大语言模型（LLMs）的使用。以往研究受限于代理指标和合成数据。本研究调查了1000名来自不同社会经济背景的人群对语言技术和生成式AI的使用情况，并收集了他们与LLM交互的6482条真实提示。结果发现，不同SES群体在使用频率、任务类型、互动风格和话题上存在系统性差异：高SES者倾向于更高层次的抽象表达，请求更简洁，关注‘包容性’和‘旅行’等主题；低SES者则表现出更高的拟人化倾向（如使用‘你好’‘谢谢’），语言更具体。尽管生成式语言技术日益普及，但社会经济相关的语言差异仍加剧了数字鸿沟。研究强调，在开发语言技术时必须考虑SES因素，以满足不同群体的语言需求，缩小不同阶层间的AI差距。
原文摘要 · Abstract (English)
Socioeconomic status (SES) fundamentally influences how people interact with each other and more recently, with digital technologies like Large Language Models (LLMs). While previous research has highlighted the interaction between SES and language technology, it was limited by reliance on proxy metrics and synthetic data. We survey 1,000 individuals from diverse socioeconomic backgrounds about their use of language technologies and generative AI, and collect 6,482 prompts from their previous interactions with LLMs. We find systematic differences across SES groups in language technology usage (i.e., frequency, performed tasks), interaction styles, and topics. Higher SES entails a higher level of abstraction, convey requests more concisely, and topics like 'inclusivity' and 'travel'. Lower SES correlates with higher anthropomorphization of LLMs (using ''hello'' and ''thank you'') and more concrete language. Our findings suggest that while generative language technologies are becoming more accessible to everyone, socioeconomic linguistic differences still stratify their use to exacerbate the digital divide. These differences underscore the importance of considering SES in developing language technologies to accommodate varying linguistic needs rooted in socioeconomic factors and limit the AI Gap across SES groups.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。