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Tailored Conversations beyond LLMs: A RL-Based Dialogue Manager

Created by
  • Haebom

Author

Lucie Galland, Catherine Pelachaud, Florian Pecune

Outline

In this paper, we propose a novel framework that integrates a large-scale language model (LLM) and a reinforcement learning-based dialogue manager for goal-oriented open-ended conversations. By leveraging hierarchical reinforcement learning to model the structural stages of a conversation and meta-learning to enhance its adaptability to different user profiles, we can learn from limited data, transition seamlessly between conversation stages, and personalize responses to heterogeneous user needs. By applying our framework to motivational interviews to promote behavioral change, we demonstrate that the proposed dialogue manager outperforms the state-of-the-art LLM baseline model in terms of rewards, thereby demonstrating the potential benefits of LLM conditioning for generating goal-oriented open-ended conversation systems.

Takeaways, Limitations

Takeaways:
A new framework for developing goal-oriented open dialogue systems
Efficient and adaptive conversation management through hierarchical reinforcement learning and meta-learning
Effective learning and personalized responses possible even with limited data
Demonstrates improved performance over LLM baseline models in conversational systems with specific goals, such as motivational interviewing.
Limitations:
Further research is needed on the generalization performance of the proposed framework and its applicability to various goal-oriented dialogue systems.
Need to analyze performance impact according to size and diversity of dataset used
Additional evaluation through interaction with real users is needed.
The results are limited to a specific domain (motivational interviewing), and generalizability to other domains needs to be verified.
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