基于用户画像动态调整对话策略，提升目标导向对话成功率
UP-NRPA: User Portrait based Nested Rollout Policy Adaptation for Planning with Large Language Models in Goal-oriented Dialogue Systems
- 根据用户画像实时调整对话策略，无需离线训练
- 多任务成功率达100%，谈判任务成交比提升56.41%
- 适合需要个性化交互的智能客服与谈判系统
为解决现有对话策略规划方法难以动态适应多样用户特征的问题，本文提出基于用户画像的嵌套回溯策略自适应（UP-NRPA）在线框架，结合大语言模型实现动态策略定制。与依赖模型训练和离线强化学习策略模型的传统方法不同，UP-NRPA通过实时用户反馈及从当前用户画像中提取的人格、偏好和目标，实现无需离线强化学习即可自适应用户特征的对话策略调整。在协作与非协作对话基准测试中表现显著，多个对话任务成功率达到100%；尤其在谈判任务中，销售对列表比率（SL）提升了56.41%。结果表明，UP-NRPA可在不依赖训练机制的情况下有效适配多样化用户需求，使对话系统具备更强的用户适应能力。
原文摘要 · Abstract (English)
To address the challenge that current dialogue policy planning methods struggle to dynamically adapt to diverse user characteristics, this paper proposes a User Portrait based Nested Rollout Policy Adaptation (UP-NRPA) online framework with Large Language Models. In contrast to conventional approaches dependent on model training and require offline reinforcement learning policy models for user groups, UP-NRPA enables dynamic customization of dialogue strategies through an adaptive mechanism. This is achieved by leveraging real-time user feedback alongside personality, preferences, and objectives mapped from the current user portrait, thereby adapting to user characteristics without offline reinforcement learning. In collaborative and non-collaborative dialogue benchmarks, UP-NRPA demonstrated considerable benefits, achieving an impressive 100% success rate in multiple dialogue tasks. Particularly in negotiation tasks, the sale-to-list ratio (SL) increased by 56.41%. This demonstrates that UP-NRPA can adapt to diverse user needs without requiring a training mechanism, enabling the dialogue system to adapt to user characteristics.
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