ReMamba: Equip Mamba with Effective Long-Sequence Modeling
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.
Paper Type: Long
Research Area: Generation
Research Area Keywords: text-to-text generation, model architectures, efficient models, inference methods
Contribution Types: NLP engineering experiment, Approaches low compute settings-efficiency
Languages Studied: english
Submission Number: 849
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