Combining Parameter-efficient Modules for Task-level Generalisation (2023.eacl-main)
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| Challenge: | A modular design encourages neural models to disentangle and recombine different facets of knowledge to generalise more systematically to new tasks. |
| Approach: | They propose a modular neural network where a subset of latent skills is associated with a parameter-efficient model adapter. |
| Outcome: | The proposed model improves sample efficiency and few-shot generalisation in supervised learning compared to baselines. |
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| Challenge: | Recent studies show that large pre-trained language models can be adapted to particular tasks in a parameter-efficient manner. |
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Jiaxing Liu, Qi Qi, Haifeng Sun, Dunjun Li, Zirui Zhuang, Bo He, Xiang Yang, Cong Liu, Jianxin Liao, Jingyu Wang
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| Challenge: | Recent efforts have explored mixtures of LoRA modules for multi-task settings, but this study reveals redundancy in the down-projection matrix of these architectures. |
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ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts (2022.emnlp-main)
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| Challenge: | a new multi-task, parameter-efficient language model tuning method learns to transfer knowledge across different tasks via a mixture of soft prompts. |
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