Cached Transformers: Improving Transformers with Differentiable Memory Cache

• URL: http://arxiv.org/abs/2312.12742v1
• Date: Wed, 20 Dec 2023 03:30:51 GMT
• Title: Cached Transformers: Improving Transformers with Differentiable Memory Cache
• Authors: Zhaoyang Zhang, Wenqi Shao, Yixiao Ge, Xiaogang Wang, Jinwei Gu, Ping Luo
• Abstract summary: This work introduces a new Transformer model called Cached Transformer. It uses Gated Recurrent Cached (GRC) attention to extend the self-attention mechanism with a differentiable memory cache of tokens.
• Score: 71.28188777209034
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work introduces a new Transformer model called Cached Transformer, which uses Gated Recurrent Cached (GRC) attention to extend the self-attention mechanism with a differentiable memory cache of tokens. GRC attention enables attending to both past and current tokens, increasing the receptive field of attention and allowing for exploring long-range dependencies. By utilizing a recurrent gating unit to continuously update the cache, our model achieves significant advancements in \textbf{six} language and vision tasks, including language modeling, machine translation, ListOPs, image classification, object detection, and instance segmentation. Furthermore, our approach surpasses previous memory-based techniques in tasks such as language modeling and displays the ability to be applied to a broader range of situations.

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