Skip-Layer Attention: Bridging Abstract and Detailed Dependencies in Transformers
<|reference_start|>Skip-Layer Attention: Bridging Abstract and Detailed Dependencies in Transformers: The Transformer architecture has significantly advanced deep learning, particularly in natural language processing, by effectively managing long-range dependencies. However, as the demand for understanding complex relationships grows, refining the Transformer's architecture becomes critical. This paper introduces Skip-Layer Attention (SLA) to enhance Transformer models by enabling direct attention between non-adjacent layers. This method improves the model's ability to capture dependencies between high-level abstract features and low-level details. By facilitating direct attention between these diverse feature levels, our approach overcomes the limitations of current Transformers, which often rely on suboptimal intra-layer attention. Our implementation extends the Transformer's functionality by enabling queries in a given layer to interact with keys and values from both the current layer and one preceding layer, thus enhancing the diversity of multi-head attention without additional computational burden. Extensive experiments demonstrate that our enhanced Transformer model achieves superior performance in language modeling tasks, highlighting the effectiveness of our skip-layer attention mechanism.<|reference_end|>
@article{chen2024skip-layer,
    title={Skip-Layer Attention: Bridging Abstract and Detailed Dependencies in
  Transformers},
    author={Qian Chen, Wen Wang, Qinglin Zhang, Siqi Zheng, Shiliang Zhang, Chong
  Deng, Hai Yu, Jiaqing Liu, Yukun Ma, Chong Zhang},
    journal={arXiv preprint arXiv:2406.11274},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.11274},
    primaryClass={cs.CL}
}