<ul><li>Long-range sequence processing poses a significant challenge for Transformers due to their quadratic complexity in input length.</li><li>Mamba, an alternative to Transformers, demonstrates high performance and achieves Transformer-level capabilities with fewer computational resources.</li><li>The length-generalization capabilities of Mamba are found to be relatively limited.</li><li>DeciMamba, a context-extension method designed for Mamba, enables the trained model to extrapolate well to longer context lengths without additional training.</li></ul>

DeciMamba: Exploring the Length Extrapolation Potential of Mamba

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