SPC: Soft Prompt Construction for Cross Domain Generalization

Wenbo Zhao, Arpit Gupta, Tagyoung Chung, Jing Huang


Abstract
Recent advances in prompt tuning have proven effective as a new language modeling paradigm for various natural language understanding tasks. However, it is challenging to adapt the soft prompt embeddings to different domains or generalize to low-data settings when learning soft prompts itself is unstable, task-specific, and bias-prone. This paper proposes a principled learning framework—soft prompt construction (SPC)—to facilitate learning domain-adaptable soft prompts. Derived from the SPC framework is a simple loss that can plug into various models and tuning approaches to improve their cross-domain performance. We show SPC can improve upon SOTA for contextual query rewriting, summarization, and paraphrase detection by up to 5%, 19%, and 16%, respectively.
Anthology ID:
2023.repl4nlp-1.10
Volume:
Proceedings of the 8th Workshop on Representation Learning for NLP (RepL4NLP 2023)
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Burcu Can, Maximilian Mozes, Samuel Cahyawijaya, Naomi Saphra, Nora Kassner, Shauli Ravfogel, Abhilasha Ravichander, Chen Zhao, Isabelle Augenstein, Anna Rogers, Kyunghyun Cho, Edward Grefenstette, Lena Voita
Venue:
RepL4NLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
118–130
Language:
URL:
https://aclanthology.org/2023.repl4nlp-1.10
DOI:
10.18653/v1/2023.repl4nlp-1.10
Bibkey:
Cite (ACL):
Wenbo Zhao, Arpit Gupta, Tagyoung Chung, and Jing Huang. 2023. SPC: Soft Prompt Construction for Cross Domain Generalization. In Proceedings of the 8th Workshop on Representation Learning for NLP (RepL4NLP 2023), pages 118–130, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
SPC: Soft Prompt Construction for Cross Domain Generalization (Zhao et al., RepL4NLP 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.repl4nlp-1.10.pdf