Mai, FlorianPannatier, ArnaudFehr, FabioChen, HaolinMarelli, FrancoisFleuret, FrancoisHenderson, James2024-05-012024-05-012024-05-012023-01-0110.18653/v1/2023.acl-long.871https://infoscience.epfl.ch/handle/20.500.14299/207634WOS:001190962507024Transformer-based architectures are the model of choice for natural language understanding, but they come at a significant cost, as they have quadratic complexity in the input length, require a lot of training data, and can be difficult to tune. In the pursuit of lower costs, we investigate simple MLP-based architectures. We find that existing architectures such as MLPMixer, which achieves token mixing through a static MLP applied to each feature independently, are too detached from the inductive biases required for natural language understanding. In this paper, we propose a simple variant, HyperMixer, which forms the token mixing MLP dynamically using hypernetworks. Empirically, we demonstrate that our model performs better than alternative MLP-based models, and on par with Transformers. In contrast to Transformers, HyperMixer achieves these results at substantially lower costs in terms of processing time, training data, and hyperparameter tuning.Technologytext::conference output::conference proceedings::conference paper