Event knowledge in large language models: the gap between the impossible and the unlikely

Understand

Word co-occurrence patterns in language corpora contain a surprising amount of conceptual knowledge.

• Large language models (LLMs), trained to predict words in context, leverage these patterns to achieve impressive performance on diverse semantic tasks requiring world knowledge.
• An important but understudied question about LLMs' semantic abilities is whether they acquire generalized knowledge of common events.
• Here, we test whether five pre-trained LLMs (from 2018's BERT to 2023's MPT) assign higher likelihood to plausible descriptions of agent-patient interactions than to minimally different implausible versions of the same event.