Language Models and Cognitive Automation for Economic Research
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.
Financial support from Brookings and from Longview Philanthropy is gratefully acknowledged. I thank Julian Hazell, Sid Srinivasan, and participants at several seminars for helpful conversations on the topic, Max Schnidman, Don Suh and Natasha Swindle for excellent research assistance, and GPT-3 and Claude for inspiration and editorial assistance. The views expressed herein are those of the author and do not necessarily reflect the views of the National Bureau of Economic Research.