What issue do LSTMs specifically address when working with traditional RNNs?

Long Short-Term Memory networks (LSTMs) were designed to effectively address the vanishing gradient problem that is often encountered in traditional Recurrent Neural Networks (RNNs). In standard RNNs, as the backpropagation through time algorithm is applied during training, the gradients can diminish exponentially with each time step. This can lead to difficulties in learning long-range dependencies because the learning signal becomes extremely weak, making it challenging for the network to adjust weights for nodes that are further back in the sequence.

LSTMs mitigate this issue by incorporating a more complex architecture that includes memory cells and gating mechanisms. These elements allow LSTMs to retain information for longer periods by controlling the flow of information. Specifically, the input, output, and forget gates within the LSTM units regulate which information is passed through the network and which information is discarded. This capability enables LSTMs to maintain and utilize relevant information over extended sequences, effectively learning patterns that span long intervals in the input data without succumbing to the vanishing gradient problem.

In summary, the strength of LSTMs lies in their ability to preserve long-term dependencies and manage the flow of gradients during training, making them particularly well-suited for tasks that require understanding of context over longer sequences,