Large Language Models at Work in China's Labor Market

• URL: http://arxiv.org/abs/2308.08776v1
• Date: Thu, 17 Aug 2023 04:20:36 GMT
• Title: Large Language Models at Work in China's Labor Market
• Authors: Qin Chen, Jinfeng Ge, Huaqing Xie, Xingcheng Xu, Yanqing Yang
• Abstract summary: This paper explores the potential impacts of large language models (LLMs) on the Chinese labor market. The results indicate a positive correlation between occupation exposure and wage levels/experience premiums. We also develop an economic growth model incorporating industry exposure to quantify the productivity-employment trade-off from AI adoption.
• Score: 4.19966590731593
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• MMAD: The First-Ever Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection [66.05200339481115]
  We present MMAD, the first-ever full-spectrum MLLMs benchmark in industrial anomaly detection. We defined seven key subtasks of MLLMs in industrial inspection and designed a novel pipeline to generate the MMAD dataset. With MMAD, we have conducted a comprehensive, quantitative evaluation of various state-of-the-art MLLMs.
  arXiv  Detail & Related papers  (2024-10-12T09:16:09Z)
• Automatic generation of insights from workers' actions in industrial workflows with explainable Machine Learning [6.354358255072839]
  There is no precise equivalent for workers' productivity. An ML solution combining data from manufacturing processes and workers' performance is required. We propose to apply explainable ML to differentiate between expert and inexpert workers.
  arXiv  Detail & Related papers  (2024-06-18T15:55:11Z)
• A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law [65.87885628115946]
  Large language models (LLMs) are revolutionizing the landscapes of finance, healthcare, and law. We highlight the instrumental role of LLMs in enhancing diagnostic and treatment methodologies in healthcare, innovating financial analytics, and refining legal interpretation and compliance strategies. We critically examine the ethics for LLM applications in these fields, pointing out the existing ethical concerns and the need for transparent, fair, and robust AI systems.
  arXiv  Detail & Related papers  (2024-05-02T22:43:02Z)
• Beyond Human Norms: Unveiling Unique Values of Large Language Models through Interdisciplinary Approaches [69.73783026870998]
  This work proposes a novel framework, ValueLex, to reconstruct Large Language Models' unique value system from scratch. Based on Lexical Hypothesis, ValueLex introduces a generative approach to elicit diverse values from 30+ LLMs. We identify three core value dimensions, Competence, Character, and Integrity, each with specific subdimensions, revealing that LLMs possess a structured, albeit non-human, value system.
  arXiv  Detail & Related papers  (2024-04-19T09:44:51Z)
• WESE: Weak Exploration to Strong Exploitation for LLM Agents [95.6720931773781]
  This paper proposes a novel approach, Weak Exploration to Strong Exploitation (WESE) to enhance LLM agents in solving open-world interactive tasks. WESE involves decoupling the exploration and exploitation process, employing a cost-effective weak agent to perform exploration tasks for global knowledge. A knowledge graph-based strategy is then introduced to store the acquired knowledge and extract task-relevant knowledge, enhancing the stronger agent in success rate and efficiency for the exploitation task.
  arXiv  Detail & Related papers  (2024-04-11T03:31:54Z)
• Enhancing PLM Performance on Labour Market Tasks via Instruction-based Finetuning and Prompt-tuning with Rules [0.9790236766474201]
  We show the effectiveness of prompt-based tuning of pre-trained language models (PLM) in labour market specific applications. Our results indicate that cost-efficient methods such as PTR and instruction tuning without exemplars can significantly increase the performance of PLMs.
  arXiv  Detail & Related papers  (2023-08-31T14:47:00Z)
• ExpeL: LLM Agents Are Experiential Learners [60.54312035818746]
  We introduce the Experiential Learning (ExpeL) agent to allow learning from agent experiences without requiring parametric updates. Our agent autonomously gathers experiences and extracts knowledge using natural language from a collection of training tasks. At inference, the agent recalls its extracted insights and past experiences to make informed decisions.
  arXiv  Detail & Related papers  (2023-08-20T03:03:34Z)
• Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models [51.3422222472898]
  We document the capability of large language models (LLMs) like ChatGPT to predict stock price movements using news headlines. We develop a theoretical model incorporating information capacity constraints, underreaction, limits-to-arbitrage, and LLMs.
  arXiv  Detail & Related papers  (2023-04-15T19:22:37Z)
• Industry Risk Assessment via Hierarchical Financial Data Using Stock Market Sentiment Indicators [0.9463895540925061]
  This paper presents an approach to analyzing industry trends leveraging real-time stock market data and generative small language models (SLMs) One of the key challenges lies in the inherent noise in raw data, which can compromise the precision of statistical analyses. We propose a dual-pronged approach to industry trend analysis: explicit and implicit analysis.
  arXiv  Detail & Related papers  (2023-03-05T16:17:56Z)
• Practical Skills Demand Forecasting via Representation Learning of Temporal Dynamics [4.536775100566484]
  Rapid technological innovation threatens to leave much of the global workforce behind. Governments and markets must find ways to quicken the rate at which the supply of skills reacts to changes in demand. This paper presents a pipeline which makes one-shot multi-step forecasts into the future using a decade of monthly skill demand observations.
  arXiv  Detail & Related papers  (2022-05-18T04:02:55Z)
• AI and Shared Prosperity [0.0]
  Future advances in AI that automate away human labor may have stark implications for labor markets and inequality. This paper proposes a framework to analyze the effects of specific types of AI systems on the labor market, based on how much labor demand they will create versus displace.
  arXiv  Detail & Related papers  (2021-05-18T12:37:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.