Frame-shifting and Conceptual Blending: What do large language models have to say?

Work in cognitive semantics suggests human language comprehension is remarkably flexible, relying heavily on hierarchically structured background knowledge and conceptual mapping ability. In this talk I will describe some evidence from my lab that suggests metrics from large language models do a good job of predicting behavioral and neural responses to some aspects of human language. I go on to describe research on joke comprehension that highlights important differences in meaning processing in humans and the ‘understanding’ displayed by language models trained only on text corpora.