UP-NRPA: User Portrait based Nested Rollout Policy Adaptation for Planning with Large Language Models in Goal-oriented Dialogue Systems
arXiv:2606.13683v1 Announce Type: new Abstract: 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
The proliferation of large language models is enabling more sophisticated approaches to dynamic dialogue policy, moving beyond static, pre-trained models. Real-time feedback mechanisms are becoming more viable as computational resources advance.
This development could significantly enhance the efficacy and user satisfaction of goal-oriented dialogue systems by tailoring interactions to individual user characteristics, improving task completion and adoption rates. For businesses, this means more effective customer service, sales, and support automation.
Dialogue systems can now adapt their strategies dynamically in real-time based on specific user profiles, rather than relying on generalized or group-based policies. This moves policy planning from static models to adaptive, user-centric frameworks.
- · AI software developers
- · Customer service platforms
- · E-commerce companies (with conversational AI)
- · Users of dialogue systems
- · Companies with static, non-adaptive dialogue systems
- · Generic chatbot providers
Goal-oriented dialogue systems become significantly more personalized and effective, leading to higher user engagement and task success rates.
Increased efficiency in automated customer support and sales, potentially reducing operational costs and improving customer lifetime value for businesses.
The development of highly personalized AI companions and assistants becomes more feasible, transforming how individuals interact with technology and access information.
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Read at arXiv cs.AI