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

Lexical Generalization Improves with Larger Models and Longer Training