UP-NRPA：基于用户画像的嵌套 rollout 策略自适应，用于目标导向对话系统中与大语言模型的规划
UP-NRPA: User Portrait based Nested Rollout Policy Adaptation for Planning with Large Language Models in Goal-oriented Dialogue Systems
AI总结：
提出 UP-NRPA 在线框架，利用大语言模型和用户画像实时自适应调整对话策略，无需离线强化学习，在协作与非协作对话基准中实现 100% 任务成功率，谈判任务销售列表比提升 56.41%。
摘要：
为了解决当前对话策略规划方法难以动态适应不同用户特征的挑战，本文提出了一种基于用户画像的嵌套 rollout 策略自适应（UP-NRPA）在线框架，结合大语言模型。与传统依赖模型训练并为用户群体离线学习强化学习策略模型的方法不同，UP-NRPA 通过自适应机制实现对话策略的动态定制。这是通过利用实时用户反馈以及从当前用户画像映射出的个性、偏好和目标来实现的，从而无需离线强化学习即可适应用户特征。在协作和非协作对话基准测试中，UP-NRPA 展现了显著优势，在多个对话任务中实现了令人印象深刻的 100% 成功率。特别是在谈判任务中，销售列表比（SL）提高了 56.41%。这表明 UP-NRPA 无需训练机制即可适应多样化的用户需求，使对话系统能够适应用户特征。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
