ReMamba: Equip Mamba with Effective Long-Sequence Modeling
This addresses long-context modeling efficiency for NLP tasks, but it is incremental as it builds on existing Mamba architecture.
The paper tackled the problem of Mamba models having limited long-context comprehension compared to transformers, and proposed ReMamba, which improved performance by 3.2 and 1.6 points on benchmarks and achieved near-parity with same-size transformers.
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.