State Space Models (SSMs) offer an efficient alternative to transformer models in handling long-range dependencies in natural language processing (NLP) by utilizing first-order differential equations to represent dynamic systems. The HiPPO framework underpins this approach, allowing SSMs to maintain continuous representations of time-dependent data. The evolution of SSMs from Linear State Space Layers (LSSL) to the S5 model, through innovations like the Structured State Space Sequence model and the Generalized Bilinear Transform, has improved computational efficiency and sequence modeling scalability. Despite their advantages, SSMs still lack the context-awareness found in transformer models' attention mechanisms, presenting a challenge for future development. Efforts like the Mamba model aim to incorporate selective focus into SSMs, potentially enhancing their applicability in NLP tasks.