State space models (SSMs) designed for modelling dynamic systems have achieved outstanding sequence-to-sequence performance in fields ranging from time series analysis to audio generation. SSMs however struggle on language modelling tasks, where they cannot match the performance of transformer architectures. In the new paper Hungry Hungry Hippos: Towards Language Modeling with State Space Models, a research team from Stanford University and the State Unive