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Status: Unchecked ×Keyword: memory cost ×Clear all

2 claims from 1 paper

1. Computer Science › Domain Adaptation and Few-Shot Learning

   Late Prompt Tuning: A Late Prompt Could Be Better Than Many Prompts

   Liu, Sun, Huang and Qiu · 2022

   The paper analyses why prompt tuning lags other efficient methods, then proposes Late Prompt Tuning, which inserts one generated prompt at an intermediate layer and is reported to match other methods with lower cost.

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   1. UncheckedAnalyses of prompt tuning suggest a trade-off between how far label signals travel to the prompt and how much the prompt influences model outputs.“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.”
   2. UncheckedLate Prompt Tuning is reported to match full model tuning and other efficient methods, in full-data and few-shot settings, while training faster and using less memory.“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.”