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.

这篇文章着重探讨了大型语言模型对人类价值、劳动力市场的影响以及需不需要对它们进行监管等问题，同时，它们也能更好地人性化技术，并且可以克服当前技术所面临的瓶颈问题。因此，我们应该更广泛地了解和推广 LLMs 以及简化 LLMs 使用的工具和方法。