We also show that manipulating the model bias term to set a semantically appropriate "default behavior" or "null hypothesis" for the model significantly improves performance.
by explicitly choosing what output a zero-information, empty explanation should correspond to, the model is able to learn explanations that correspond more closely with human-generated data.