Papers with Perturbation-based regularizer

1 papers
PTP: Boosting Stability and Performance of Prompt Tuning with Perturbation-Based Regularizer (2023.emnlp-main)

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Challenge: Existing prompt tuning methods have training instability issues due to large variance of scores . existing prompt tuning algorithms have training stability issues due a slight change of input data .
Approach: They propose an algorithm that smooths the loss landscape of vanilla prompt tuning by perturbation-based regularizers.
Outcome: The proposed method improves the state-of-the-art prompt tuning methods by 1.94% and 2.34% on SuperGLUE and FewGLUE benchmarks.

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