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        <title>Late Prompt Tuning: A Late Prompt Could Be Better Than Many Prompts</title>
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        <namePart type="given">Xiangyang</namePart>
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    <name type="personal">
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        <namePart type="given">Xipeng</namePart>
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    <abstract>Prompt tuning is a parameter-efficient tuning (PETuning) method for utilizing pre-trained models (PTMs) that simply prepends a soft prompt to the input and only optimizes the prompt to adapt PTMs to downstream tasks. Although it is parameter- and deployment-efficient, its performance still lags behind other state-of-the-art PETuning methods. Besides, the training cost of prompt tuning is not significantly reduced due to the back-propagation through the entire model. Through empirical analyses, we shed some light on the lagging performance of prompt tuning and recognize a trade-off between the propagation distance from label signals to the inserted prompt and the influence of the prompt on model outputs. Further, we present Late Prompt Tuning (LPT) that inserts a late prompt into an intermediate layer of the PTM instead of the input layer or all layers. The late prompt is obtained by a neural prompt generator conditioned on the hidden states before the prompt insertion layer and therefore is instance-dependent. Through extensive experimental results across various tasks and PTMs, we show that LPT can achieve competitive performance to full model tuning and other PETuning methods under both full-data and few-shot scenarios while possessing faster training speed and lower memory cost.</abstract>
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    <identifier type="doi">10.18653/v1/2022.findings-emnlp.95</identifier>
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%0 Conference Proceedings
%T Late Prompt Tuning: A Late Prompt Could Be Better Than Many Prompts
%A Liu, Xiangyang
%A Sun, Tianxiang
%A Huang, Xuanjing
%A Qiu, Xipeng
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F liu-etal-2022-late
%X Prompt tuning is a parameter-efficient tuning (PETuning) method for utilizing pre-trained models (PTMs) that simply prepends a soft prompt to the input and only optimizes the prompt to adapt PTMs to downstream tasks. Although it is parameter- and deployment-efficient, its performance still lags behind other state-of-the-art PETuning methods. Besides, the training cost of prompt tuning is not significantly reduced due to the back-propagation through the entire model. Through empirical analyses, we shed some light on the lagging performance of prompt tuning and recognize a trade-off between the propagation distance from label signals to the inserted prompt and the influence of the prompt on model outputs. Further, we present Late Prompt Tuning (LPT) that inserts a late prompt into an intermediate layer of the PTM instead of the input layer or all layers. The late prompt is obtained by a neural prompt generator conditioned on the hidden states before the prompt insertion layer and therefore is instance-dependent. Through extensive experimental results across various tasks and PTMs, we show that LPT can achieve competitive performance to full model tuning and other PETuning methods under both full-data and few-shot scenarios while possessing faster training speed and lower memory cost.
%R 10.18653/v1/2022.findings-emnlp.95
%U https://aclanthology.org/2022.findings-emnlp.95
%U https://doi.org/10.18653/v1/2022.findings-emnlp.95
%P 1325-1338

Markdown (Informal)

[Late Prompt Tuning: A Late Prompt Could Be Better Than Many Prompts](https://aclanthology.org/2022.findings-emnlp.95) (Liu et al., Findings 2022)

• Late Prompt Tuning: A Late Prompt Could Be Better Than Many Prompts (Liu et al., Findings 2022)

ACL

• Xiangyang Liu, Tianxiang Sun, Xuanjing Huang, and Xipeng Qiu. 2022. Late Prompt Tuning: A Late Prompt Could Be Better Than Many Prompts. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 1325–1338, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.