APrompt: Attention Prompt Tuning for Efficient Adaptation of Pre-trained Language Models (2023.emnlp-main)
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Qifan Wang, Yuning Mao, Jingang Wang, Hanchao Yu, Shaoliang Nie, Sinong Wang, Fuli Feng, Lifu Huang, Xiaojun Quan, Zenglin Xu, Dongfang Liu
| Challenge: | Existing prompt tuning methods only introduce prompts at the input layer, limiting performance and leaving large room for improvement. |
| Approach: | They propose a method that involves tuning a small set of soft prompts for pre-trained language models. |
| Outcome: | The proposed method outperforms state-of-the-art methods with pre-trained models on the SuperGLUE benchmark. |
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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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Yuan Yao, Bowen Dong, Ao Zhang, Zhengyan Zhang, Ruobing Xie, Zhiyuan Liu, Leyu Lin, Maosong Sun, Jianyong Wang
| Challenge: | Recent studies have shown promising results of prompt tuning in stimulating pre-trained language models (PLMs) for natural language processing tasks. |
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| Challenge: | a recent study has shown that fine-tuning pre-trained models is parameter-inefficient and expensive. |
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| Challenge: | Prompt tuning has emerged as a successful parameter-efficient alternative to the full fine-tuning of language models. |
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