What are the main challenges of training very large Transformer models?

The main challenges of training very large Transformer models stem from computational limitations, memory constraints, and the risk of overfitting. Computationally, training these models requires significant processing power due to the sheer number of parameters and the complexity of the self-attention mechanism. This translates to longer training times and the need for specialized hardware, such as GPUs or TPUs. Memory constraints also pose a major challenge. Large models require a significant amount of memory to store the model parameters, intermediate activations, and gradients during training. This can exceed the memory capacity of a single device, necessitating techniques like model parallelism or gradient accumulation to distribute the workload across multiple devices. Overfitting is another major concern. Larger models have a greater capacity to memorize the training data, making them more susceptible to overfitting, especially when the training data is limited. Effective regularization techniques, such as dropout, weight decay, and label smoothing, are crucial to prevent overfitting and ensure good generalization performance. Furthermore, training large models can be unstable, requiring careful tuning of hyperparameters such as the learning rate, batch size, and optimizer settings. Optimizing these hyperparameters can be a time-consuming and resource-intensive process. Finally, evaluating the performance of large models can also be challenging, as it requires a large amount of data to obtain reliable estimates of the model's generalization performance.