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Generative AI for Economic Research: LLMs Learn to Collaborate and Reason

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  • Anton Korinek

Abstract

Large language models (LLMs) have seen remarkable progress in speed, cost efficiency, accuracy, and the capacity to process larger amounts of text over the past year. This article is a practical guide to update economists on how to use these advancements in their research. The main innovations covered are (i) new reasoning capabilities, (ii) novel workspaces for interactive LLM collaboration such as Claude's Artifacts, ChatGPT's Canvas or Microsoft's Copilot, and (iii) recent improvements in LLM-powered internet search. Incorporating these capabilities in their work allows economists to achieve significant productivity gains. Additionally, I highlight new use cases in promoting research, such as automatically generated blog posts, presentation slides and interviews as well as podcasts via Google's NotebookLM.

Suggested Citation

  • Anton Korinek, 2024. "Generative AI for Economic Research: LLMs Learn to Collaborate and Reason," NBER Working Papers 33198, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:33198
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    JEL classification:

    • A10 - General Economics and Teaching - - General Economics - - - General
    • B4 - Schools of Economic Thought and Methodology - - Economic Methodology
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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