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Language Models and Cognitive Automation for Economic Research

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  • Anton Korinek

Abstract

Large language models (LLMs) such as ChatGPT have the potential to revolutionize research in economics and other disciplines. I describe 25 use cases along six domains in which LLMs are starting to become useful as both research assistants and tutors: ideation, writing, background research, data analysis, coding, and mathematical derivations. I provide general instructions and demonstrate specific examples for how to take advantage of each of these, classifying the LLM capabilities from experimental to highly useful. I hypothesize that ongoing advances will improve the performance of LLMs across all of these domains, and that economic researchers who take advantage of LLMs to automate micro tasks will become significantly more productive. Finally, I speculate on the longer-term implications of cognitive automation via LLMs for economic research.

Suggested Citation

  • Anton Korinek, 2023. "Language Models and Cognitive Automation for Economic Research," NBER Working Papers 30957, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:30957
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    Cited by:

    1. Anton Korinek & Donghyun Suh, 2024. "Scenarios for the Transition to AGI," NBER Working Papers 32255, National Bureau of Economic Research, Inc.
    2. Alejandro Lopez-Lira & Yuehua Tang, 2023. "Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models," Papers 2304.07619, arXiv.org, revised Sep 2024.
    3. Gary Charness & Brian Jabarian & John List, 2023. "Generation Next: Experimentation with AI," Artefactual Field Experiments 00777, The Field Experiments Website.
    4. Yuhao Fu & Nobuyuki Hanaki, 2024. "Do people rely on ChatGPT more than their peers to detect fake news?," ISER Discussion Paper 1233, Institute of Social and Economic Research, Osaka University.
    5. Tyna Eloundou & Sam Manning & Pamela Mishkin & Daniel Rock, 2023. "GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models," Papers 2303.10130, arXiv.org, revised Aug 2023.
    6. Nir Chemaya & Daniel Martin, 2023. "Perceptions and Detection of AI Use in Manuscript Preparation for Academic Journals," Papers 2311.14720, arXiv.org, revised Jan 2024.
    7. Ardekani, Aref Mahdavi & Bertz, Julie & Bryce, Cormac & Dowling, Michael & Long, Suwan(Cheng), 2024. "FinSentGPT: A universal financial sentiment engine?," International Review of Financial Analysis, Elsevier, vol. 94(C).
    8. Jakub Growiec, 2023. "Industry 4.0? Framing the Digital Revolution and Its Long-Run Growth Consequences," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 4, pages 1-16.
    9. Alonso-Robisco, Andres & Carbó, José Manuel, 2023. "Analysis of CBDC narrative by central banks using large language models," Finance Research Letters, Elsevier, vol. 58(PC).
    10. Baptiste Lefort & Eric Benhamou & Jean-Jacques Ohana & David Saltiel & Beatrice Guez & Thomas Jacquot, 2024. "Stress index strategy enhanced with financial news sentiment analysis for the equity markets," Papers 2404.00012, arXiv.org.
    11. Niszczota, Paweł & Abbas, Sami, 2023. "GPT has become financially literate: Insights from financial literacy tests of GPT and a preliminary test of how people use it as a source of advice," Finance Research Letters, Elsevier, vol. 58(PA).
    12. Tanvir Ahmed Khan, 2023. "Can Unbiased Predictive AI Amplify Bias?," Working Paper 1510, Economics Department, Queen's University.
    13. Wallusch Jacek, 2023. "Pricing and data science: The tale of two accidentally parallel transitions," Economics and Business Review, Sciendo, vol. 9(2), pages 115-132, April.
    14. Pedro H. Albuquerque & Sophie Albuquerque, 2023. "Social Implications of Technological Disruptions: A Transdisciplinary Cybernetics Science and Occupational Science Perspective," AMSE Working Papers 2313, Aix-Marseille School of Economics, France.
    15. Baptiste Lefort & Eric Benhamou & Jean-Jacques Ohana & David Saltiel & Beatrice Guez, 2024. "Optimizing Performance: How Compact Models Match or Exceed GPT's Classification Capabilities through Fine-Tuning," Papers 2409.11408, arXiv.org.

    More about this item

    JEL classification:

    • A10 - General Economics and Teaching - - General Economics - - - General
    • B41 - Schools of Economic Thought and Methodology - - Economic Methodology - - - Economic Methodology
    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights

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