  Given the increasingly prominent role NLP models (will) play in our lives, it
is important to evaluate models on their alignment with human expectations of
how models behave. Using Natural Language Inference (NLI) as a case study, we
investigated the extent to which human-generated explanations of models'
inference decisions align with how models actually make these decisions. More
specifically, we defined two alignment metrics that quantify how well natural
language human explanations align with model sensitivity to input words, as
measured by integrated gradients. Then, we evaluated six different transformer
models (the base and large versions of BERT, RoBERTa and ELECTRA), and found
that the BERT-base model has the highest alignment with human-generated
explanations, for both alignment metrics. Additionally, the base versions of
the models we surveyed tended to have higher alignment with human-generated
explanations than their larger counterparts, suggesting that increasing the
number model parameters could result in worse alignment with human
explanations. Finally, we find that a model's alignment with human explanations
is not predicted by the model's accuracy on NLI, suggesting that accuracy and
alignment are orthogonal, and both are important ways to evaluate models.
