GPTs are GPTs: 劳动力市场影响潜力初探
原文链接: https://arxiv.org/abs/2303.10130
原文：GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models
Title
GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models 标题（中文）： GPTs are GPTs：大型语言模型对劳动力市场影响潜力的初步审视
Authors
Tyna Eloundou, Sam Manning, Pamela Mishkin, Daniel Rock 作者（中文）： Tyna Eloundou、Sam Manning、Pamela Mishkin、Daniel Rock
Abstract
We investigate the potential implications of large language models (LLMs), such as Generative Pre-trained Transformers (GPTs), on the U.S. labor market, focusing on the increased capabilities arising from LLM-powered software compared to LLMs on their own. Using a new rubric, we assess occupations based on their alignment with LLM capabilities, integrating both human expertise and GPT-4 classifications. Our findings reveal that around 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of LLMs, while approximately 19% of workers may see at least 50% of their tasks impacted. We do not make predictions about the development or adoption timeline of such LLMs. The projected effects span all wage levels, with higher-income jobs potentially facing greater exposure to LLM capabilities and LLM-powered software. Significantly, these impacts are not restricted to industries with higher recent productivity growth. Our analysis suggests that, with access to an LLM, about 15% of all worker tasks in the US could be completed significantly faster at the same level of quality. When incorporating software and tooling built on top of LLMs, this share increases to between 47 and 56% of all tasks. This finding implies that LLM-powered software will have a substantial effect on scaling the economic impacts of the underlying models. We conclude that LLMs such as GPTs exhibit traits of general-purpose technologies, indicating that they could have considerable economic, social, and policy implications.
摘要（中文）： 我们研究了大型语言模型（LLM），如生成式预训练Transformer（GPT），对美国劳动力市场的潜在影响，重点关注由LLM驱动软件相较于单独LLM所带来的能力提升。我们使用一套新的评估框架，根据职业与LLM能力的匹配程度进行评估，整合了人类专业知识和GPT-4的分类结果。我们的发现表明，约80%的美国劳动者至少有10%的工作任务可能受到LLM引入的影响，约19%的劳动者可能有至少50%的任务受到影响。我们不对此类LLM的发展或采用时间线做出预测。预期影响覆盖所有工资水平，高收入工作可能面临更大的LLM能力和LLM驱动软件的暴露程度。值得注意的是，这些影响并不局限于近期生产力增长较高的行业。我们的分析表明，在使用LLM的情况下，美国约15%的劳动者任务可以以同等质量显著更快地完成。当将建立在LLM之上的软件和工具纳入考量时，这一比例上升至所有任务的47%至56%。这一发现意味着LLM驱动的软件将对底层模型的经济影响规模化产生重大作用。我们的结论是，GPT等LLM表现出通用技术（general-purpose technologies）的特征，表明它们可能产生重大的经济、社会和政策影响。
Subjects
General Economics (econ.GN); Artificial Intelligence (cs.AI); Computers and Society (cs.CY) 学科（中文）： 一般经济学（econ.GN）；人工智能（cs.AI）；计算与社会（cs.CY）
Submission History
- v1 Fri, 17 Mar 2023: 首次提交
- v5 Mon, 21 Aug 2023: 最终修订版
提交历史（中文）：
- 第一版 2023年3月17日（周五）：首次提交
- 第五版 2023年8月21日（周一）：最终修订版
Key Findings
| Metric | Value | |--------|-------| | 美国劳动者至少有10%任务受影响 | ~80% | | 美国劳动者至少50%任务受影响 | ~19% | | 使用LLM后任务显著加速完成 | ~15% | | 结合LLM驱动软件后任务加速完成 | 47-56% |
关键发现（中文）：
| 指标 | 数值 | |------|------| | 美国劳动者至少有10%任务受影响 | 约80% | | 美国劳动者至少50%任务受影响 | 约19% | | 使用LLM后任务可显著加速完成 | 约15% | | 结合LLM驱动软件后任务可加速完成 | 47-56% |
*注：本文为arXiv摘要页面，完整论文请访问 https://arxiv.org/abs/2303.10130*