In Depth
Long Short-Term Memory (LSTM) networks, introduced by Hochreiter and Schmidhuber in 1997, solve the vanishing gradient problem that limits basic RNNs. They use a cell state (a kind of conveyor belt of information) regulated by three gates: the forget gate decides what information to discard, the input gate decides what new information to store, and the output gate decides what to pass to the next step.
This gating mechanism allows LSTMs to selectively remember or forget information across hundreds of time steps, making them effective at tasks requiring long-range memory. Before the transformer era, LSTMs were the dominant architecture for machine translation, speech recognition, text generation, and time-series forecasting.
While transformers have replaced LSTMs for most large-scale language tasks, LSTMs remain valuable in specific applications. They excel at real-time time-series prediction, audio processing, and embedded systems where their sequential processing and constant memory usage are advantages rather than limitations. Understanding LSTMs provides important context for appreciating why transformers represented such a significant architectural breakthrough.