  How do language models "think"? This paper formulates a probabilistic
cognitive model called the bounded pragmatic speaker, which can characterize
the operation of different variations of language models. Specifically, we
demonstrate that large language models fine-tuned with reinforcement learning
from human feedback (Ouyang et al., 2022) embody a model of thought that
conceptually resembles a fast-and-slow model (Kahneman, 2011), which
psychologists have attributed to humans. We discuss the limitations of
reinforcement learning from human feedback as a fast-and-slow model of thought
and propose avenues for expanding this framework. In essence, our research
highlights the value of adopting a cognitive probabilistic modeling approach to
gain insights into the comprehension, evaluation, and advancement of language
models.
