Papers by Varun Kanade
Simplicity Bias in Transformers and their Ability to Learn Sparse Boolean Functions (2023.acl-long)
Copied to clipboard
| Challenge: | Recent studies have found that Transformers struggle to model several formal languages when compared to recurrent models. |
| Approach: | They conduct an extensive empirical study on Boolean functions to demonstrate that Transformers are relatively more biased towards functions of low sensitivity . they also show that Transformer's generalize near perfectly even in the presence of noisy labels whereas recurrent models overfit and achieve poor generalization accuracy. |
| Outcome: | The results show that Transformers generalize near perfectly even in noisy Boolean functions whereas recurrent models overfit and achieve poor generalization accuracy. |