Persuading across Diverse Domains: a Dataset and Persuasion Large Language Model

Chuhao Jin, Kening Ren, Lingzhen Kong, Xiting Wang, Ruihua Song, Huan Chen


Abstract
Persuasive dialogue requires multi-turn following and planning abilities to achieve the goal of persuading users, which is still challenging even for state-of-the-art large language models (LLMs). Previous works focus on retrieval-based models or generative models in a specific domain due to a lack of data across multiple domains. In this paper, we leverage GPT-4 to create the first multi-domain persuasive dialogue dataset DailyPersuasion. Then we propose a general method named PersuGPT to learn a persuasion model based on LLMs through intent-to-strategy reasoning, which summarizes the intent of user’s utterance and reasons next strategy to respond. Moreover, we design a simulation-based preference optimization, which utilizes a learned user model and our model to simulate next turns and estimate their rewards more accurately. Experimental results on two datasets indicate that our proposed method outperforms all baselines in terms of automatic evaluation metric Win-Rate and human evaluation. The code and data are available at https://persugpt.github.io.
Anthology ID:
2024.acl-long.92
Volume:
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1678–1706
Language:
URL:
https://aclanthology.org/2024.acl-long.92
DOI:
10.18653/v1/2024.acl-long.92
Bibkey:
Cite (ACL):
Chuhao Jin, Kening Ren, Lingzhen Kong, Xiting Wang, Ruihua Song, and Huan Chen. 2024. Persuading across Diverse Domains: a Dataset and Persuasion Large Language Model. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1678–1706, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
Persuading across Diverse Domains: a Dataset and Persuasion Large Language Model (Jin et al., ACL 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.acl-long.92.pdf