  Current large language models can perform reasonably well on complex tasks
that require step-by-step reasoning with few-shot learning. Are these models
applying reasoning skills they have learnt during pre-training and reason
outside of their training context, or are they simply memorizing their training
corpus at finer granularity and have learnt to better understand their context?
To tease apart these possibilities, we introduce ALERT, a benchmark and suite
of analyses for assessing language models' reasoning ability comparing
pre-trained and finetuned models on complex tasks that require reasoning skills
to solve. ALERT provides a test bed to asses any language model on fine-grained
reasoning skills, which spans over 20 datasets and covers 10 different
reasoning skills. We leverage ALERT to further investigate the role of
finetuning. With extensive empirical analysis we find that language models
learn more reasoning skills such as textual entailment, abductive reasoning,
and analogical reasoning during finetuning stage compared to pretraining state.
We also find that when language models are finetuned they tend to overfit to
the prompt template, which hurts the robustness of models causing
generalization problems.
