Recovering Mental Representations from Large Language Models with Markov
  Chain Monte Carlo
Jian-Qiao Zhu Haijiang Yan Thomas L. Griffiths
Simulating sampling algorithms with people has proven a useful method for
efficiently probing and understanding their mental representations. We propose
that the same methods can be used to study the representations of Large
Language Models (LLMs). While one can always directly prompt either humans or
LLMs to disclose their mental representations introspectively, we show that
increased efficiency can be achieved by using LLMs as elements of a sampling
algorithm. We explore the extent to which we recover human-like representations
when LLMs are interrogated with Direct Sampling and Markov chain Monte Carlo
(MCMC). We found a significant increase in efficiency and performance using
adaptive sampling algorithms based on MCMC. We also highlight the potential of
our method to yield a more general method of conducting Bayesian inference
\textit{with} LLMs.