Long-Context Models

Long Short-Term Memory Networks (LSTMs) are advanced recurrent neural networks that effectively model sequential data by overcoming traditional RNN limitations like vanishing gradients. They utilize gating mechanisms—input, forget, and output gates—to maintain and update information over extended sequences, enabling better capture of long-term dependencies, which is crucial in applications such as language modeling, speech recognition, and time series analysis.