@inproceedings{zhao-etal-2023-spc,
    title = "{SPC}: Soft Prompt Construction for Cross Domain Generalization",
    author = "Zhao, Wenbo  and
      Gupta, Arpit  and
      Chung, Tagyoung  and
      Huang, Jing",
    editor = "Can, Burcu  and
      Mozes, Maximilian  and
      Cahyawijaya, Samuel  and
      Saphra, Naomi  and
      Kassner, Nora  and
      Ravfogel, Shauli  and
      Ravichander, Abhilasha  and
      Zhao, Chen  and
      Augenstein, Isabelle  and
      Rogers, Anna  and
      Cho, Kyunghyun  and
      Grefenstette, Edward  and
      Voita, Lena",
    booktitle = "Proceedings of the 8th Workshop on Representation Learning for NLP (RepL4NLP 2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.repl4nlp-1.10",
    doi = "10.18653/v1/2023.repl4nlp-1.10",
    pages = "118--130",
    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.",
}

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.
Zhao, Wenbo Gupta, Arpit Chung, Tagyoung Huang, Jing
SPC: Soft Prompt Construction for Cross Domain Generalization