Why are language models less surprised than humans? Testing the Parse Multiplicity Mismatch Hypothesis

ArXi:2605.15440v1 Announce Type: new Surprisal theory posits that the processing difficulty of a word is determined by its predictability in context, offering a potential link between human sentence processing and next-word predictions from language models. While language model (LM) surprisals successfully predict reading times in naturalistic text, they systematically underpredict the magnitude of difficulty observed in controlled studies of syntactic ambiguity, particularly in garden path sentences.