PTP: Boosting Stability and Performance of Prompt Tuning with Perturbation-Based Regularizer

Lichang Chen, Jiuhai Chen, Heng Huang, Minhao Cheng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Recent studies show that prompt tuning can better leverage the power of large language models than fine-tuning on downstream natural language understanding tasks. Nonetheless, current prompt tuning methods encounter instability during training, marked by a high variance in scores given different random seeds. In addressing this crucial issue, we uncover that the loss landscape of standard prompt tuning, when visualized, is remarkably steep, i.e., minor alterations in the input data can trigger substantial fluctuations in the loss landscape, which is an essential factor that leads to the training instability. In light of this finding, we incorporate perturbation-based regularizers to temper the loss landscape within the prompt tuning process. We thus present a novel algorithm, called Prompt Tuning with Perturbation-based regularizer (PTP), that can significantly reduce training instability and concurrently enhance the performance of prompt tuning. Specifically, we design two variants of perturbation-based regularizers: one that employs random noise, and another that uses an adversarial approach. Importantly, our proposed perturbations display flexibility in both the text and embedding spaces. Extensive experiments show the effectiveness of our proposed methods in stabilizing the training. Our new algorithms improve the state-of-the-art prompt tuning methods by 1.94% and 2.34% on SuperGLUE and FewGLUE benchmarks, respectively.

Original languageEnglish (US)
Title of host publicationEMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings
EditorsHouda Bouamor, Juan Pino, Kalika Bali
PublisherAssociation for Computational Linguistics (ACL)
Pages13512-13525
Number of pages14
ISBN (Electronic)9798891760608
StatePublished - 2023
Event2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023 - Hybrid, Singapore, Singapore
Duration: Dec 6 2023Dec 10 2023

Publication series

NameEMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings

Conference

Conference2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023
Country/TerritorySingapore
CityHybrid, Singapore
Period12/6/2312/10/23

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems
  • Linguistics and Language

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