  While fine-tuned language models perform well on many tasks, they were also
shown to rely on superficial surface features such as lexical overlap.
Excessive utilization of such heuristics can lead to failure on challenging
inputs. We analyze the use of lexical overlap heuristics in natural language
inference, paraphrase detection, and reading comprehension (using a novel
contrastive dataset), and find that larger models are much less susceptible to
adopting lexical overlap heuristics. We also find that longer training leads
models to abandon lexical overlap heuristics. Finally, we provide evidence that
the disparity between models size has its source in the pre-trained model
