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arXiv · 2408.15496

ReMamba: Equip Mamba with Effective Long-Sequence Modeling

Danlong Yuan·Jiahao Liu·Bei Li·Huishuai Zhang·Jingang Wang·Xunliang Cai·Dongyan Zhao
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Abstract

While the Mamba architecture demonstrates superior inference efficiency and competitive performance on short-context natural language processing (NLP) tasks, empirical evidence suggests its capacity to comprehend long contexts is limited compared to transformer-based models. In this study, we investigate the long-context efficiency issues of the Mamba models and propose ReMamba, which enhances Mamba's ability to comprehend long contexts. ReMamba incorporates selective compression and adaptation techniques within a two-stage re-forward process, incurring minimal additional inference costs overhead. Experimental results on the LongBench and L-Eval benchmarks demonstrate ReMamba's efficacy, improving over the baselines by 3.2 and 1.6 points, respectively, and attaining performance almost on par with same-size transformer models.

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Danlong Yuan, Jiahao Liu, Bei Li, Huishuai Zhang, Jingang Wang, Xunliang Cai, Dongyan Zhao. 2024-08-28. ReMamba: Equip Mamba with Effective Long-Sequence Modeling. https://arxiv.org/abs/2408.15496

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