As of my last update in September 2021, "overloading" in the context of transformers typically refers to pushing the model beyond its intended capacity, which can have both positive and negative effects on performance. Here are the key effects of overloading on transformer performance:
Improved Performance: Overloading a transformer model by increasing its size (number of layers, hidden units, attention heads, etc.) and parameters can often lead to improved performance on various tasks. A larger model can capture more complex patterns in the data, allowing it to learn more intricate relationships and perform better on challenging tasks.
Increased Memory and Computation Requirements: Larger transformer models consume more memory and computational resources during training and inference. This can be a significant concern as it might lead to difficulties in deploying and serving the model efficiently, especially in resource-constrained environments.
Longer Training Time: As the model size increases, the training time also tends to increase significantly. Training larger models on large datasets can take days or even weeks, making it more challenging to experiment with different architectures and hyperparameters.
Overfitting: Overloading a transformer with excessive capacity on relatively small datasets can lead to overfitting. The model might memorize the training data instead of generalizing to new, unseen examples, resulting in poor performance on test data.
Regularization Challenges: Regularization techniques like dropout and weight decay might be less effective or require tuning when using overloaded models. Striking the right balance between capacity and regularization is crucial to avoid overfitting.
Transferability: Overloaded models may not generalize as well across different tasks or domains. A more general and compact model might be better suited for transfer learning scenarios.
Interpretability and Explainability: Larger models tend to be less interpretable and harder to explain, as they have more parameters and layers, making it challenging to understand their decision-making process.
Environmental Impact: Training larger models consumes a significant amount of computational power and energy, which has raised concerns about their environmental impact.
It's essential to carefully consider the trade-offs between model capacity, performance, and resource requirements when deciding on the appropriate size for a transformer model. For different tasks and datasets, the optimal model size may vary. Transfer learning from pre-trained models like GPT-3, BERT, or T5 is also an option to leverage the benefits of larger models without the need for training them from scratch. Researchers continue to explore techniques to improve the efficiency and scalability of transformer models to address some of the challenges posed by overloading.