Large Language Models (LLMs) have made rapid progress in recent months and weeks, garnering significant public attention. This has sparked concerns about aligning these models with human values, their impact on labor markets, and the potential need for regulation in further research and development. However, the discourse often lacks a focus on the imperative to widely diffuse the societal benefits of LLMs. To qualify this societal benefit, we assert that LLMs exhibit emergent abilities to humanize technology more effectively than previous technologies, and for people across language, occupation, and accessibility divides. We argue that they do so by addressing three mechanizing bottlenecks in today's computing technologies: creating diverse and accessible content, learning complex digital tools, and personalizing machine learning algorithms. We adopt a case-based approach and illustrate each bottleneck with two examples where current technology imposes bottlenecks that LLMs demonstrate the ability to address. Given this opportunity to humanize technology widely, we advocate for more widespread understanding of LLMs, tools and methods to simplify use of LLMs, and cross-cutting institutional capacity.

翻译：大型语言模型（LLMs）在近数月及数周内取得了快速进展，引发了广泛公众关注。这带来了一系列担忧：如何使这些模型与人类价值观对齐、其对劳动力市场的影响，以及进一步研发中可能需要制定的监管措施。然而，现有讨论往往缺乏对广泛普及LLMs社会效益这一迫切需求的关注。为明确这种社会效益，我们主张：LLMs展现出前所未有的能力，能比以往技术更有效地赋予技术人性化特质，并跨越语言、职业和可及性差异，惠及不同人群。我们认为，LLMs通过解决当前计算技术中的三大机械化瓶颈来实现这一目标：创建多样化且可访问的内容、学习复杂的数字工具、以及个性化机器学习算法。我们采用案例研究方法，针对每个瓶颈列举两个实例，说明当前技术存在的局限性以及LLMs应对这些局限性的能力。鉴于LLMs具备广泛赋予技术人性化特质的机遇，我们呼吁加强对LLMs的普及认知、开发简化其使用的工具与方法，并建立跨领域的制度能力。