What are the advantages and disadvantages of using long short-term memory (LSTM) cells over simple RNN cells?

Readers should first understand the “exploding-gradient” problem. It is not unique to the standard RNN and, indeed, also becomes a common problem with other RNN structures such as Long Short Term Memory (LSTM). The exploding gradient problem occurs when large error gradients accumulate during training over time, resulting in very large updates to the neural network’s model weights (i.e., large, erroneous updates). The resulting NN-weights are then completely distorted, rendering the model unstable and effectively useless, as it is unable to learn properly from newly input training data.

LSTM Cells enhance RNNs by addressing issues with long-term dependencies. RNNs: Process each input in sequence with a hidden layer. The output is compared to the target, and errors are used to update weights and biases via backpropagation through time (BPTT). LSTMs: Introduce a memory cell and three gates (input, forget, and output) to manage long-term dependencies: Input Gate: Controls the amount of new information added to the memory cell. Forget Gate: Determines how much of the old memory cell to discard. Output Gate: Decides how much of the memory cell and hidden state to use for the output.

To avoid misleading the reader, it is important to understand that LSTMs do *not* necessarily avoid the exploding gradient problem. While LSTMs do indeed resolve the problem with vanishing gradients, as described above, they do *not* escape exploding gradients. These large error gradients, when applied to the LSTM network’s weights, distorts the models interpretation of inputs and renders it useless, incapable of learning from further training data.

Standard RNNs struggle with processing long sequences, encountering problems like vanishing and exploding gradients, destabilizing training, and leading to poor models. Constructed as a variant of RNNs, LSTMs are explicitly designed to circumvent the vanishing gradient problem. Their ability to retain information over extended time frames makes them efficient for long sequences. The effectiveness of LSTM networks in retaining information over long periods comes from their unique gating mechanism, which controls the flow of data into, within, and out of the LSTM cell, thereby preserving information over time and mitigating the problem of vanishing gradients. However, LSTMS are not entirely free from the problem of gradients.

Complexity in implementation: The LSTM architecture is more complex than a simple RNN, which can make it harder to implement and understand. Risk of overfitting: Due to their large number of parameters and complex architecture, LSTMs can be prone to overfitting, especially when the available training data is limited. Gradient explosion: Although LSTMs alleviate the vanishing gradient problem, they can still suffer from gradient explosion, especially when the sequence length is very long. Limited interpretability: LSTMs are often seen as "black-box" models, making it difficult to interpret and understand their decision-making process.