EdgeFormer: A Parameter-Efficient Transformer for On-Device Seq2seq Generation (2022.emnlp-main)
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| Challenge: | Extensive experiments show EdgeFormer can effectively outperform previous parameter-efficient Transformer baselines and achieve competitive results under both the computation and memory constraints. |
| Approach: | They propose a parameter-efficient Transformer for on-device seq2seq generation that uses two novel principles for cost-effective parameterization. |
| Outcome: | Extensive experiments show that EdgeFormer outperforms the previous parameter-efficient Transformers and achieves competitive results under both the computation and memory constraints. |
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| Challenge: | Existing methods for inference in centralized cloud pose privacy risks due to sensitive data. |
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Transformer and seq2seq model for Paraphrase Generation (D19-56)
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HashFormers: Towards Vocabulary-independent Pre-trained Transformers (2022.emnlp-main)
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| Challenge: | Improving Transformer efficiency has become increasingly attractive in recent years. |
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Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks (2021.acl-long)
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| Challenge: | State-of-the-art parameter-efficient fine-tuning methods rely on introducing adapter modules between the layers of a pretrained language model. |
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Seq2seq is All You Need for Coreference Resolution (2023.emnlp-main)
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AdaKron: An Adapter-based Parameter Efficient Model Tuning with Kronecker Product (2024.lrec-main)
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| Challenge: | Large Pretrained Language Models (PLMs) have billions of parameters, causing computational challenges to fine-tuning models. |
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