Clozing the Gap: Exploring Why Language Model Surprisal Outperforms Cloze Surprisal 事件

Clozing the Gap: Exploring Why Language Model Surprisal Outperforms Cloze Surprisal arXiv:2601.09886v2 Announce Type: replace Abstract: How predictable a word is can be quantified in two ways: using human responses to the cloze task or using probabilities from language models (LMs).When used as predictors of processing effort, LM probabilities outperform probabilities derived from cloze data. However, it is important to establish that LM probabilities do so for the right reasons, since differen