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If the same neural network architecture is trained multiple times on the samedataset, will it make similar linguistic generalizations across runs? To studythis question, we fine-tuned 100 instances of BERT on the Multi-genre NaturalLanguage Inference (MNLI) dataset and evaluated them on the HANS dataset, whichevaluates syntactic generalization in natural language inference. On the MNLIdevelopment set, the behavior of all instances was remarkably consistent, withaccuracy ranging between 83.6% and 84.8%. In stark contrast, the same modelsvaried widely in their generalization performance. For example, on the simplecase of subject-object swap (e.g., determining that "the doctor visited thelawyer" does not entail "the lawyer visited the doctor"), accuracy ranged from0.00% to 66.2%. Such variation is likely due to the presence of many localminima that are equally attractive to a low-bias learner such as a neuralnetwork; decreasing the variability may therefore require models with strongerinductive biases.

BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performance