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Recovering Mental Representations from Large Language Models with Markov Chain Monte Carlo
Simulating sampling algorithms with people has proven a useful method for efficiently probing and understanding their mental representations.
Incoherent Probability Judgments in Large Language Models
Autoregressive Large Language Models (LLMs) trained for next-word prediction have demonstrated remarkable proficiency at producing coherent text.
Deep de Finetti: Recovering Topic Distributions from Large Language Models
Large language models (LLMs) can produce long, coherent passages of text, suggesting that LLMs, although trained on next-word prediction, must represent the latent structure that characterizes a document.
Bayes in the age of intelligent machines
The success of methods based on artificial neural networks in creating intelligent machines seems like it might pose a challenge to explanations of human cognition in terms of Bayesian inference.
Mental Sampling in Multimodal Representations
We propose that mental sampling is not done by simple MCMC, but is instead adapted to multimodal representations and is implemented by Metropolis-coupled Markov chain Monte Carlo (MC$^3$), one of the first algorithms developed for sampling from multimodal distributions.
