The Structural Sources of Verb Meanings Revisited: Large Language Models Display Syntactic Bootstrapping

Abstract

Syntactic bootstrapping (Gleitman, 1990) is the hypothesis that children use the syntactic environments in which a verb occurs to learn its meaning. Existing evidence for this hypothesis generally involves controlled experimental settings (e.g., Jin & Fisher, 2014; Naigles, 1990; Yuan et al., 2012). In this paper, we leverage the powerful statistical learning abilities of neural language models to examine the contribution of different kinds of cues for word learning when a learner is presented with a corpus of English linguistic input on the scale of a child’s experience. We do this by training two standard types of neural networks (masked and autoregressive transformers) on perturbed datasets where different sources of information relevant to word learning are individually removed. Our results show that models’ verb representations degrade more when we perturb word order, which removes syntactic information, than when we perturb co-occurrence among content words, which removes semantic information. Furthermore, the representations of mental verbs, for which syntactic bootstrapping has been shown to be particularly crucial in human verb learning, are more negatively impacted in the training regime with perturbed word order as compared to physical verbs. In contrast, while word order also affects models’ representations of nouns, the nouns are influenced more by co-occurrence information than the verbs are. In addition to reinforcing the important role of syntactic bootstrapping in verb learning, our results demonstrate the viability of testing developmental hypotheses on a large scale through manipulating the learning environments of large language models.