Gated Slot Attention for Efficient Linear-Time Sequence Modeling

Yu Zhang, Songlin Yang, Rui-Jie Zhu, Yue Zhang, Leyang Cui, Yiqiao Wang, Bolun Wang, Freda Shi, Bailin Wang, Wei Bi, Peng Zhou, Guohong Fu

Keywords: sequence modeling, language modeling, hardware-efficient, linear attention

Abstract:

Linear attention Transformers and their gated variants, celebrated for enabling parallel training and efficient recurrent inference, still fall short in recall-intensive tasks compared to traditional Transformers and demand significant resources for training from scratch.
This paper introduces Gated Slot Attention (GSA), which enhances Attention with Bounded-memory-Control (ABC) by incorporating a gating mechanism inspired by Gated Linear Attention (GLA).
Essentially, GSA comprises a two-layer GLA linked via $\operatorname{softmax}$, utilizing context-aware memory reading and adaptive forgetting to improve memory capacity while maintaining compact recurrent state size.
This design greatly enhances both training and inference efficiency through GLA's hardware-efficient training algorithm and reduced state size.
Additionally, retaining the $\operatorname{softmax}$ operation is particularly beneficial in ``finetuning pretrained Transformers to RNNs'' (T2R) settings, reducing the need for extensive training from scratch.
Extensive experiments confirm GSA's superior performance in scenarios requiring in-context recall and in T2R settings.

Primary Area: Natural language processing

Submission Number: 15000