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

Anton Korinek

No 33198, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: A10 B4 C88 O33 (search for similar items in EconPapers)
Date: 2024-11
New Economics Papers: this item is included in nep-ain, nep-big and nep-cmp
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