Model Soup

Model souping is a technique that improves the performance and robustness of machine learning models by averaging the weights of multiple models trained with different hyperparameters, effectively creating a "soup" of model parameters. Current research focuses on applying this method to various architectures, including large language models (LLMs), vision transformers (ViTs), and graph neural networks (GNNs), exploring its benefits for tasks like cross-lingual transfer, out-of-distribution generalization, and adversarial robustness. This approach offers a computationally efficient way to enhance model accuracy and generalization capabilities, impacting both research and practical applications by improving the performance of existing models without significantly increasing inference time or computational cost.