The LSTM model is considered a special case of RNNs. The problems of vanishing gradients and exploding gradients we saw earlier are a disadvantage while using the plain RNN model.
In LSTMs, we add a forget gate, which is basically a memory unit that retains information that is retained across timesteps and discards the other information that is not needed. This also necessitates the need for input and output gates to include the results of the forget gate as well.
