What BERT is not: Lessons from a new suite of psycholinguistic diagnostics for language models
Table 14: Percent of NEG-88-NAT with true continuation given higher probability than false. Aff = affirmative, Neg = negative. NT = natural, LN = less natural.
['[EMPTY]', 'Aff. NT', 'Neg. NT', 'Aff. LN', 'Neg. LN']
The supplementary NEG-88-NAT items allow us to examine further the model’s handling of negation, with items designed to test the effect of “naturalness”. When we present BERT with this new set of sentences, the model does show an apparent change in sensitivity to the negation. BERTBASE assigns true statements higher probability than false for 75% of natural sentences (“NT”), and BERTLARGE does so for 87.5% of natural sentences. By contrast, the models each show preference for true statements in only 37.5% of items designed to be less natural (“LN”). Here we see that in the natural sentences, BERT prefers true statements for both affirmative and negative contexts—by contrast, the less natural sentences show the pattern exhibited on NEG-88-SIMP, in which BERT prefers true statements in a high proportion of affirmative sentences, and in 0% of negative sentences, suggesting that once again BERT is defaulting to category matches with the subject.