Ojasva Nema, Kaustubh Sharma, Aditya Chauhan, Parikshit Pareek
Bilinear MLPs with multiplicative interactions improve structural disentanglement and model editability by leveraging architectural inductive bias for better representation and unlearning capabilities.
Modern neural networks often struggle with unlearning specific information and generalizing to new situations, even when dealing with tasks that have clear mathematical structures. This study suggests that these issues are not just about how we optimize or unlearn information, but also about how the network organizes its internal representations. By using a specific type of neural network architecture that includes multiplicative interactions, the researchers found that the network can better separate and organize information. This approach helps the network to 'unlearn' certain aspects more effectively and improve its generalization abilities.
