GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models
The study "GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models" by Tyna Eloundou, Sam Manning, Pamela Mishkin and Daniel Rock explores the potential implications of large-scale language models (LLMs), such as Generative Pre-trained Transformers (GPTs), on the U.S. labor market. The study focuses on the enhanced capabilities resulting from software powered by LLMs compared to LLMs by themselves.
The research team developed a new classification system to evaluate occupations based on their alignment with LLMs' capabilities. This system integrated both human experience and GPT-4 ratings. The findings revealed that about 80% of the U.S. workforce could see at least 10% of their job tasks affected by the introduction of LLMs. Likewise, about 19% of workers could see at least 50% of their job tasks impacted.
The authors make no predictions about the development or timing of the adoption of LLMs. The projected effects span all wage levels, with higher income jobs potentially facing greater exposure to the capabilities of LLMs and LLM-powered software. Significantly, these impacts are not limited to industries with higher recent productivity growth.
The analysis suggests that, with access to an LLM, about 15% of all worker tasks in the U.S. could be completed significantly faster while maintaining the same level of quality. When software and tools built on top of LLMs are incorporated, this proportion increases to between 47% and 56% of all tasks. This finding implies that software powered by LLMs will have a substantial effect on extending the economic impacts of the underlying models.
The study concludes that LLMs such as GPTs exhibit characteristics of general-purpose technologies, indicating that they could have considerable economic, social, and political implications. The findings underscore the need for a deeper understanding and careful regulation of these emerging technologies to maximize their benefits and minimize potential labor market harms.
I have written this article simply using the content of the paper: "GPT's are GPTS's".
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