Adele Goldberg

Leonie Weissweiler, Kyle Mahowald, Adele Goldberg

Linguistic evaluations of how well LMs generalize to produce or understand language often implicitly take for granted that natural languages are generated by symbolic rules. According to this perspective, grammaticality is determined by whether sentences obey such rules. Interpretation is compositionally generated by syntactic rules operating on meaningful words. Semantic parsing maps sentences into formal logic. Failures of LMs to obey strict rules are presumed to reveal that LMs do not produce or understand language like humans. Here we suggest that LMs' failures to obey symbolic rules may be a feature rather than a bug, because natural languages are not based on neatly separable, compositional rules. Rather, new utterances are produced and understood by a combination of flexible, interrelated, and context-dependent constructions. Considering gradient factors such as frequencies, context, and function will help us reimagine new benchmarks and analyses to probe whether and how LMs capture the rich, flexible generalizations that comprise natural languages.

Supantho Rakshit, Adele Goldberg

The usage-based constructionist (UCx) approach to language posits that language comprises a network of learned form-meaning pairings (constructions) whose use is largely determined by their meanings or functions, requiring them to be graded and probabilistic. This study investigates whether the internal representations in Large Language Models (LLMs) reflect the proposed function-infused gradience. We analyze representations of the English Double Object (DO) and Prepositional Object (PO) constructions in Pythia-$1.4$B, using a dataset of $5000$ sentence pairs systematically varied by human-rated preference strength for DO or PO. Geometric analyses show that the separability between the two constructions' representations, as measured by energy distance or Jensen-Shannon divergence, is systematically modulated by gradient preference strength, which depends on lexical and functional properties of sentences. That is, more prototypical exemplars of each construction occupy more distinct regions in activation space, compared to sentences that could have equally well have occured in either construction. These results provide evidence that LLMs learn rich, meaning-infused, graded representations of constructions and offer support for geometric measures for representations in LLMs.