Long-term dependencies, wherein distant tokens in a sequence exhibit significant interdependence, pose a fundamental challenge in language modeling. To address this challenge, language models leverage specialized architectural components, such as attention mechanisms and recurrent neural networks (RNNs). Attention mechanisms enable the model to selectively attend to relevant tokens across long input sequences, facilitating more effective information propagation and context integration. Similarly, RNNs, particularly variants such as Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs), are adept at capturing temporal dependencies and preserving information over extended sequences, thereby enhancing the model’s ability to handle long-range contextual relationships.
