Language Models and Cognitive Automation for Economic Research

By Anton Korinek. 2023

Subfields: Large Language Models, Economics & Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Large Language Models

1. RosaI. Arriaga
2. Pamela Mishkin
3. Gati Aher
4. Adam Tauman Kalai

In Economics & Operations Research

1. Tyna Eloundou
2. Pamela Mishkin
3. Sam Manning
4. RosaI. Arriaga
5. Daniel Rock
6. Gati Aher
7. Adam Tauman Kalai

Citations in the corpus

• GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models (2023, background): “There is also a growing set of studies examining specific economic impacts and opportunities for LLMs (Bommasani et al., 2021; Felten et al., 2023; Korinek, 2023; Mollick and Mollick, 2022; Noy and Zhang, 2023; Peng et al., 2023).”
• Can AI language models replace human participants? (2023, unknown): “Gener-ative large language models (LLMs) like GPT are replacing much human labor, including in psychological science, where researchers use LLMs to help edit papers, conduct literature reviews, and create scale items [2].”
• Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies (2022, methodology): “Several works propose ways to use LMs as proxies for a diverse set of humans, such as cheaply automating a variety of small writing tasks (Korinek, 2023) and using prompts to generate synthetic human-like interactions with desired properties (Park et al., 2022; Caron & Srivastava, 2022; Jiang et…”
• Steering Language Models With Activation Engineering (2023, unknown): “LLMs contain hidden capabilities we do not know how to fully elicit (Korinek, 2023).”