  Probabilistic models of language understanding are valuable tools for
investigating human language use. However, they need to be hand-designed for a
particular domain. In contrast, large language models (LLMs) are trained on
text that spans a wide array of domains, but they lack the structure and
interpretability of probabilistic models. In this paper, we use
chain-of-thought prompts to introduce structures from probabilistic models into
LLMs. We explore this approach in the case of metaphor understanding. Our
chain-of-thought prompts lead language models to infer latent variables and
reason about their relationships in order to choose appropriate paraphrases for
metaphors. The latent variables and relationships chosen are informed by
theories of metaphor understanding from cognitive psychology. We apply these
prompts to the two largest versions of GPT-3 and show that they can improve
performance in a paraphrase selection task.
