Rarely a problem? Language models exhibit inverse scaling in their predictions following few-type quantifiers
James A. Michaelov, Benjamin K. Bergen · 2023
How well do language models deal with quantification?In this study, we focus on few-type quantifiers, as in few children like toys, which might pose a particular challenge for language models because the sentence components without the quantifier are likely to co-occur, and few-type quantifiers are rare.We present 960 English sentence stimuli from two human neurolinguistic experiments to 22 autoregressive transformer models of differing sizes.Not only do all the models perform poorly on few-type quantifiers, but overall the larger the model, the worse its performance.This inverse scaling is consistent with previous work suggesting that larger models increasingly reflect online rather than offline human processing, and we argue that the decreasing performance of larger models may challenge uses of language models as the basis for natural language systems.