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P-Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasks
Open accessAbstract
Prompt tuning, which only tunes continuous prompts with a frozen language model, substantially reduces per-task storage and memory usage at training. However, in the context of NLU, prior work reveals that prompt tuning does not perform well for normal-sized pretrained models. We also find that existing methods of prompt tuning cannot handle hard sequence labeling tasks, indicating a lack of universality. We present a novel empirical finding that properly optimized prompt tuning can be universally effective across a wide range of model scales and NLU tasks. It matches the performance of finetuning while having only 0.1%-3% tuned parameters. Our method P-Tuning v2 is an implementation of Deep Prompt Tuning (CITATION) optimized and adapted for NLU. Given the universality and simplicity of P-Tuning v2, we believe it can serve as an alternative to finetuning and a strong baseline for future research.
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Research works printed but not linked: 4
- P-Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasks https://aclanthology.org/2022.acl-short.8/ (Liu et al., ACL 2022)
- P-Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasks (Liu et al., ACL 2022)
- Xiao Liu, Kaixuan Ji, Yicheng Fu, Weng Tam, Zhengxiao Du, Zhilin Yang, and Jie Tang. 2022. P-Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasks. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 61–68, Dublin, Ireland. Association for Computational Linguistics.
- ACL materials are Copyright © 1963–2026 ACL; other materials are copyrighted by their respective copyright holders. Materials prior to 2016 here are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License. Permission is granted to make copies for the purposes of teaching and research. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License.
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Metadata
- Authors
- Liu, X., Ji, K., Fu, Y., Tam, W., Du, Z., Yang, Z., & Tang, J.
- Affiliations
- Beijing Academy of Artificial Intelligence (CN)
- Tsinghua University (CN)
- Shanghai Qi Zhi Institute (CN)
- Corresponding author
- Liu, Xiao
- Venue
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Published
- 2022
- Pages
- 61-68
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W4285247752
- Cited by
- 810
- References
- 29
- Citations by year
- 2026: 112 · 2025: 232 · 2024: 265 · 2023: 173 · 2022: 28
- FWCI
- 96.50
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- Funders
- National Natural Science Foundation of China (61836013, 61825602)
- Tsinghua University
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- prompt tuning, fine-tuning alternatives, natural language understanding, sequence labeling, model scale, memory efficiency, parameter-efficient training
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