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        <title>LayerConnect: Hypernetwork-Assisted Inter-Layer Connector to Enhance Parameter Efficiency</title>
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    <abstract>Pre-trained Language Models (PLMs) are the cornerstone of the modern Natural Language Processing (NLP). However, as PLMs become heavier, fine tuning all their parameters loses their efficiency. Existing parameter-efficient methods generally focus on reducing the trainable parameters in PLMs but neglect the inference speed, which limits the ability to deploy PLMs. In this paper, we propose LayerConnect (hypernetwork-assisted inter-layer connectors) to enhance inference efficiency. Specifically, a light-weight connector with a linear structure is inserted between two Transformer layers, and the parameters inside each connector are tuned by a hypernetwork comprising an interpolator and a down-sampler. We perform extensive experiments on the widely used the GLUE benchmark. The experimental results verify the inference efficiency of our model. Compared to Adapter, our model parameters are reduced to approximately 11.75%, while the performance degradation is kept to less than 5% (2.5 points on average).</abstract>
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%0 Conference Proceedings
%T LayerConnect: Hypernetwork-Assisted Inter-Layer Connector to Enhance Parameter Efficiency
%A Shi, Haoxiang
%A Zhang, Rongsheng
%A Wang, Jiaan
%A Wang, Cen
%A Zheng, Yinhe
%A Sakai, Tetsuya
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F shi-etal-2022-layerconnect
%X Pre-trained Language Models (PLMs) are the cornerstone of the modern Natural Language Processing (NLP). However, as PLMs become heavier, fine tuning all their parameters loses their efficiency. Existing parameter-efficient methods generally focus on reducing the trainable parameters in PLMs but neglect the inference speed, which limits the ability to deploy PLMs. In this paper, we propose LayerConnect (hypernetwork-assisted inter-layer connectors) to enhance inference efficiency. Specifically, a light-weight connector with a linear structure is inserted between two Transformer layers, and the parameters inside each connector are tuned by a hypernetwork comprising an interpolator and a down-sampler. We perform extensive experiments on the widely used the GLUE benchmark. The experimental results verify the inference efficiency of our model. Compared to Adapter, our model parameters are reduced to approximately 11.75%, while the performance degradation is kept to less than 5% (2.5 points on average).
%U https://aclanthology.org/2022.coling-1.276
%P 3120-3126

Markdown (Informal)

[LayerConnect: Hypernetwork-Assisted Inter-Layer Connector to Enhance Parameter Efficiency](https://aclanthology.org/2022.coling-1.276) (Shi et al., COLING 2022)

• LayerConnect: Hypernetwork-Assisted Inter-Layer Connector to Enhance Parameter Efficiency (Shi et al., COLING 2022)

ACL

• Haoxiang Shi, Rongsheng Zhang, Jiaan Wang, Cen Wang, Yinhe Zheng, and Tetsuya Sakai. 2022. LayerConnect: Hypernetwork-Assisted Inter-Layer Connector to Enhance Parameter Efficiency. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3120–3126, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.