UP-NRPA: User Portrait based Nested Rollout Policy Adaptation for Planning with Large Language Models in Goal-oriented Dialogue Systems
Quick Answer
This paper shows that The UP-NRPA framework utilizes Large Language Models for dynamic dialogue policy adaptation based on real-time user feedback, achieving a 100% success rate in various tasks and a 56.41% increase in negotiation sale-to-list ratios, without requiring offline reinforcement learning.
Quick Take
The UP-NRPA framework utilizes Large Language Models for dynamic dialogue policy adaptation based on real-time user feedback, achieving a 100% success rate in various tasks and a 56.41% increase in negotiation sale-to-list ratios, without requiring offline reinforcement learning.
Key Points
- UP-NRPA adapts dialogue strategies using real-time user portraits.
- Achieved 100% success rate across collaborative and non-collaborative benchmarks.
- Negotiation tasks saw a 56.41% increase in sale-to-list ratios.
- Eliminates the need for offline reinforcement learning models.
- Dynamic customization enhances user experience in goal-oriented dialogues.
Paper Resources
Article Excerpt
From source RSS / original summaryarXiv: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 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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