Understanding and Enhancing Mamba-Transformer Hybrids for Memory Recall and Language Modeling
This work provides incremental insights for researchers and practitioners designing hybrid models for memory recall and language modeling tasks.
The paper tackled the problem of insufficient understanding in hybrid state space model-attention architectures by analyzing memory utilization and performance, revealing that sequential hybrids excel in shorter contexts while parallel hybrids are better for longer contexts, and introduced a data-centric training method with paraphrased datasets that enhances recall and outperforms architectural modifications.
Hybrid models that combine state space models (SSMs) with attention mechanisms have shown strong performance by leveraging the efficiency of SSMs and the high recall ability of attention. However, the architectural design choices behind these hybrid models remain insufficiently understood. In this work, we analyze hybrid architectures through the lens of memory utilization and overall performance, and propose a complementary method to further enhance their effectiveness. We first examine the distinction between sequential and parallel integration of SSM and attention layers. Our analysis reveals several interesting findings, including that sequential hybrids perform better on shorter contexts, whereas parallel hybrids are more effective for longer contexts. We also introduce a data-centric approach of continually training on datasets augmented with paraphrases, which further enhances recall while preserving other capabilities. It generalizes well across different base models and outperforms architectural modifications aimed at enhancing recall. Our findings provide a deeper understanding of hybrid SSM-attention models and offer practical guidance for designing architectures tailored to various use cases. Our findings provide a deeper understanding of hybrid SSM-attention models and offer practical guidance for designing architectures tailored to various use cases.