Hadamard Adapter

Hadamard adapters are a parameter-efficient method for adapting pre-trained language models (and other models) to specific downstream tasks, minimizing computational cost and storage requirements while maintaining performance. Research focuses on improving the efficiency and generalization capabilities of these adapters, exploring architectures like Kronecker product-based modules and weight averaging techniques across multiple adapters to enhance performance on diverse tasks. This approach is significant because it allows for the effective utilization of large pre-trained models in resource-constrained environments and facilitates more efficient transfer learning across different modalities and domains. The resulting improvements in efficiency and adaptability have broad implications for various AI applications.