What is the primary limitation of Recurrent Neural Networks when processing long sequences?

Recurrent Neural Networks (RNNs) are particularly designed to handle sequential data by maintaining a hidden state that captures information about previous time steps. However, one of their primary limitations when processing long sequences is their difficulty in managing long-range dependencies, which arises due to the vanishing gradient problem.

In the context of training RNNs, the vanishing gradient problem occurs during backpropagation, particularly when the gradients of the loss function need to be propagated back through many layers or time steps. As the network backpropagates the error to adjust weights, the gradients can become exponentially smaller, leading to a scenario where the network effectively “forgets” information from earlier time steps in the sequence. This makes it challenging for the RNN to learn relationships or dependencies that span over longer sequences, as the influence of earlier inputs diminishes significantly.

In contrast, while other options address various aspects of RNNs, they do not directly reflect this core characteristic. For example, although RNNs may require a considerable amount of data or can suffer from computational inefficiencies, these issues are not as fundamentally limiting to their architecture when compared to the challenge of learning long-range dependencies. Thus, the struggle with long-range dependencies due to the vanishing gradient problem