<![CDATA[Transformer architectures have dominated natural language processing (NLP) advancements in recent years, yet their growing computational demands and challenges in robustness motivate exploration of alternative models. This study qualitatively evaluates State-Space Models (SSMs) as a promising next-generation architecture for NLP tasks. By conducting a comprehensive literature analysis and comparative examination of current research, this paper investigates SSMs' theoretical foundations, robustness to input perturbations, efficiency in handling long sequences, and applicability to diverse linguistic contexts. The results show that SSMs offer compelling advantages over Transformers in memory efficiency and sequence modeling capacity, while demonstrating competitive or superior robustness in several NLP benchmarks, highlighting their potential as efficient, scalable, and robust alternatives for future NLP applications.]]>
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