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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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Prompt Tuning on Graph-Augmented Low-Resource Text Classification
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DictPrompt: Comprehensive dictionary-integrated prompt tuning for pre-trained language model
Cao, R., Wang, Y., Gao, L. C., & Yang, M. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110605 · Cited by 15
Li, D., & Zhang, Z. (2023) · PLoS ONE · doi:10.1371/journal.pone.0293034 · Cited by 16
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FaultProfIT: Hierarchical Fault Profiling of Incident Tickets in Large-scale Cloud Systems
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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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