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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.11274 |
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| _version_ | 1866929388028690432 |
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| author | Chen, Qian Wang, Wen Zhang, Qinglin Zheng, Siqi Zhang, Shiliang Deng, Chong Yu, Hai Liu, Jiaqing Ma, Yukun Zhang, Chong |
| author_facet | Chen, Qian Wang, Wen Zhang, Qinglin Zheng, Siqi Zhang, Shiliang Deng, Chong Yu, Hai Liu, Jiaqing Ma, Yukun Zhang, Chong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_11274 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Skip-Layer Attention: Bridging Abstract and Detailed Dependencies in Transformers Chen, Qian Wang, Wen Zhang, Qinglin Zheng, Siqi Zhang, Shiliang Deng, Chong Yu, Hai Liu, Jiaqing Ma, Yukun Zhang, Chong Computation and Language 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. |
| title | Skip-Layer Attention: Bridging Abstract and Detailed Dependencies in Transformers |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2406.11274 |