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    <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.</abstract>
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%0 Conference Proceedings
%T Towards Zero-Shot Persona Dialogue Generation with In-Context Learning
%A Xu, Xinchao
%A Lei, Zeyang
%A Wu, Wenquan
%A Niu, Zheng-Yu
%A Wu, Hua
%A Wang, Haifeng
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F xu-etal-2023-towards-zero
%X 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.
%R 10.18653/v1/2023.findings-acl.90
%U https://aclanthology.org/2023.findings-acl.90
%U https://doi.org/10.18653/v1/2023.findings-acl.90
%P 1387-1398

Markdown (Informal)

[Towards Zero-Shot Persona Dialogue Generation with In-Context Learning](https://aclanthology.org/2023.findings-acl.90) (Xu et al., Findings 2023)

• Towards Zero-Shot Persona Dialogue Generation with In-Context Learning (Xu et al., Findings 2023)

ACL

• Xinchao Xu, Zeyang Lei, Wenquan Wu, Zheng-Yu Niu, Hua Wu, and Haifeng Wang. 2023. Towards Zero-Shot Persona Dialogue Generation with In-Context Learning. In Findings of the Association for Computational Linguistics: ACL 2023, pages 1387–1398, Toronto, Canada. Association for Computational Linguistics.