  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 with
out 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.
