Eliciting the Priors of Large Language Models using Iterated In-Context
Learning
Eliciting the Priors of Large Language Models using Iterated In-Context
Learning
As Large Language Models (LLMs) are increasingly deployed in real-world settings, understanding the knowledge they implicitly use when making decisions is critical. One way to capture this knowledge is in the form of Bayesian prior distributions. We develop a prompt-based workflow for eliciting prior distributions from LLMs. Our approach is …