Varied number of bit split: To generate the samples in this split, we first sampled the number ofbits, then sampled each bitindividually from auniform Bernoulli distribution. Variednumberofonessplit: Here, we fixed the number of bits at30. NaturalLanguageParityDataset: Inorder totapinto thenatural language understanding capabilities of pretrained language models, we situated the parity task as a"coin flip problem". We trained baseline models with the same parameter count on a modified version of the variable assignment dataset where the order of the operations were randomly shuffled. We used greedy decoding in all of our experiments (including few-shot scratchpad ones).

We define an easy and hard split.

Moreover,doctors explicitly explore severepathologies before potentially ruling them out from the differential, especially in acute care settings. Finally, for doctors to trust a system's recommendations, they need to understand how the gathered evidences led to the predicted diseases.