Large language models (LLMs) have seen remarkable progress in speed, cost efficiency, accuracy, and the capacity to process larger amounts of text ov

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

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2024-11-26 23:00:07

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.

I would like to thank Hemanth Asirvatham, Paul Bousquet, Kevin Bryan, Alan Chan, and Sam Manning for helpful comments on this paper, and David Romer for wisely asking me to commit to producing regular updates of this rapidly evolving material when publishing my first paper on the topic in the Dec.�2023 issue of the JEL. I have learned so much from working on these updates! All remaining errors are my own. I gratefully acknowledge financial support from the Complexity Science Hub Vienna, where part of this research was conducted. The views expressed herein are those of the author and do not necessarily reflect the views of the National Bureau of Economic Research.

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