This paper explores the potential impacts of large language models (LLMs) on the Chinese labor market. We analyze occupational exposure to LLM capabilities by incorporating human expertise and LLM classifications, following Eloundou et al. (2023)'s methodology. We then aggregate occupation exposure to the industry level to obtain industry exposure scores. The results indicate a positive correlation between occupation exposure and wage levels/experience premiums, suggesting higher-paying and experience-intensive jobs may face greater displacement risks from LLM-powered software. The industry exposure scores align with expert assessments and economic intuitions. We also develop an economic growth model incorporating industry exposure to quantify the productivity-employment trade-off from AI adoption. Overall, this study provides an analytical basis for understanding the labor market impacts of increasingly capable AI systems in China. Key innovations include the occupation-level exposure analysis, industry aggregation approach, and economic modeling incorporating AI adoption and labor market effects. The findings will inform policymakers and businesses on strategies for maximizing the benefits of AI while mitigating adverse disruption risks.

翻译：本文探讨了大型语言模型（LLMs）对中国劳动力市场的潜在影响。我们借鉴Eloundou等（2023）的方法，结合人类专家知识和大语言模型分类，分析了职业对大语言模型能力的暴露程度。随后，我们将职业暴露程度聚合至行业层面，以获取行业暴露评分。结果表明，职业暴露程度与工资水平/经验溢价呈正相关，表明高薪和经验密集型工作可能面临由大语言模型驱动软件带来的更大替代风险。行业暴露评分与专家评估及经济学直觉相吻合。我们还构建了一个包含行业暴露因素的经济增长模型，以量化人工智能应用所带来的生产率与就业之间的权衡。总体而言，本研究为理解中国日益强大的人工智能系统对劳动力市场的影响提供了分析基础。关键创新包括职业层面的暴露分析、行业聚合方法以及融合人工智能应用与劳动力市场影响的经济建模。研究结果将为政策制定者和企业制定策略提供依据，以在最大化人工智能效益的同时减轻其潜在的颠覆性风险。