  Recently, transformer-based methods such as RoBERTa and GPT-3 have led to
significant experimental advances in natural language processing tasks such as
question answering and commonsense reasoning. The latter is typically evaluated
through multiple benchmarks framed as multiple-choice instances of the former.
According to influential leaderboards hosted by the Allen Institute (evaluating
state-of-the-art performance on commonsense reasoning benchmarks), models based
on such transformer methods are approaching human-like performance and have
average accuracy well over 80% on many benchmarks. Since these are commonsense
benchmarks, a model that generalizes on commonsense reasoning should not
experience much performance loss across multiple commonsense benchmarks. In
this paper, we study the generalization issue in detail by designing and
conducting a rigorous scientific study. Using five common benchmarks, multiple
controls and statistical analysis, we find clear evidence that fine-tuned
commonsense language models still do not generalize well, even with moderate
changes to the experimental setup, and may, in fact, be susceptible to dataset
bias. We also perform selective studies, including qualitative and consistency
analyses, to gain deeper insight into the problem.
