In Depth
Recurrent Neural Networks (RNNs) process sequences by maintaining an internal state that acts as a form of memory. At each step, the network takes the current input and its previous hidden state to produce an output and a new hidden state. This allows RNNs to, in principle, capture dependencies across arbitrarily long sequences of text, audio, time series, or other sequential data.
In practice, basic RNNs suffer from the vanishing and exploding gradient problems, making it difficult to learn long-range dependencies. When gradients are propagated backward through many time steps, they tend to shrink to near zero (vanishing) or grow uncontrollably (exploding). This limitation led to the development of gated architectures like LSTM and GRU, which use learned gates to control information flow.
While RNNs dominated sequence modeling for years, they have been largely superseded by transformers for most natural language processing tasks. Transformers process all positions in parallel rather than sequentially, enabling much faster training on modern hardware. However, RNNs and their variants remain relevant for real-time streaming applications, certain time-series tasks, and recent hybrid architectures like RWKV and Mamba that combine RNN-like efficiency with transformer-like performance.