@inproceedings{xu-etal-2023-towards-zero,
    title = "Towards Zero-Shot Persona Dialogue Generation with In-Context Learning",
    author = "Xu, Xinchao  and
      Lei, Zeyang  and
      Wu, Wenquan  and
      Niu, Zheng-Yu  and
      Wu, Hua  and
      Wang, Haifeng",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.90",
    doi = "10.18653/v1/2023.findings-acl.90",
    pages = "1387--1398",
    abstract = "Much work has been done to improve persona consistency by finetuning a pretrained dialogue model on high-quality human-annoated persona datasets. However, these methods still face the challenges of high cost and poor scalability. To this end, we propose a simple-yet-effective approach to significantly improve zero-shot persona consistency via in-context learning. Specifically, we first pre-train a persona-augmented dialogue generation model and then utilize in-context prompting mechanism to realize zero-shot persona customization. Experimental results demonstrate that our method can dramatically improve persona consistency without compromising coherence and informativeness in zero-shot settings.",
}

Much work has been done to improve persona consistency by finetuning a pretrained dialogue model on high-quality human-annoated persona datasets. However, these methods still face the challenges of high cost and poor scalability. To this end, we propose a simple-yet-effective approach to significantly improve zero-shot persona consistency via in-context learning. Specifically, we first pre-train a persona-augmented dialogue generation model and then utilize in-context prompting mechanism to realize zero-shot persona customization. Experimental results demonstrate that our method can dramatically improve persona consistency without compromising coherence and informativeness in zero-shot settings.
Xu, Xinchao Lei, Zeyang Wu, Wenquan Niu, Zheng-Yu Wu, Hua Wang, Haifeng
Towards Zero-Shot Persona Dialogue Generation with In-Context Learning