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Model interpretability methods are often used to explain NLP model decisionson tasks such as text classification, where the output space is relativelysmall. However, when applied to language generation, where the output spaceoften consists of tens of thousands of tokens, these methods are unable toprovide informative explanations. Language models must consider variousfeatures to predict a token, such as its part of speech, number, tense, orsemantics. Existing explanation methods conflate evidence for all thesefeatures into a single explanation, which is less interpretable for humanunderstanding.  To disentangle the different decisions in language modeling, we focus onexplaining language models contrastively: we look for salient input tokens thatexplain why the model predicted one token instead of another. We demonstratethat contrastive explanations are quantifiably better than non-contrastiveexplanations in verifying major grammatical phenomena, and that theysignificantly improve contrastive model simulatability for human observers. Wealso identify groups of contrastive decisions where the model uses similarevidence, and we are able to characterize what input tokens models use duringvarious language generation decisions.

Interpreting Language Models with Contrastive Explanations