Simplicity Bias in Transformers and their Ability to Learn Sparse Boolean Functions
This work addresses the problem of understanding why Transformers generalize well in practice despite limited expressiveness, which is incremental but provides quantifiable evidence for researchers in machine learning.
The study investigated Transformers' ability to learn sparse Boolean functions, finding that they generalize near perfectly with noisy labels, achieving high accuracy, while LSTMs overfit and perform poorly.
Despite the widespread success of Transformers on NLP tasks, recent works have found that they struggle to model several formal languages when compared to recurrent models. This raises the question of why Transformers perform well in practice and whether they have any properties that enable them to generalize better than recurrent models. In this work, we conduct an extensive empirical study on Boolean functions to demonstrate the following: (i) Random Transformers are relatively more biased towards functions of low sensitivity. (ii) When trained on Boolean functions, both Transformers and LSTMs prioritize learning functions of low sensitivity, with Transformers ultimately converging to functions of lower sensitivity. (iii) On sparse Boolean functions which have low sensitivity, we find that Transformers generalize near perfectly even in the presence of noisy labels whereas LSTMs overfit and achieve poor generalization accuracy. Overall, our results provide strong quantifiable evidence that suggests differences in the inductive biases of Transformers and recurrent models which may help explain Transformer's effective generalization performance despite relatively limited expressiveness.