Tailored Conversations beyond LLMs: A RL-Based Dialogue Manager
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911044167794688 |
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| author | Galland, Lucie Pelachaud, Catherine Pecune, Florian |
| author_facet | Galland, Lucie Pelachaud, Catherine Pecune, Florian |
| contents | In this work, we propose a novel framework that integrates large language models (LLMs) with an RL-based dialogue manager for open-ended dialogue with a specific goal. By leveraging hierarchical reinforcement learning to model the structured phases of dialogue and employ meta-learning to enhance adaptability across diverse user profiles, our approach enhances adaptability and efficiency, enabling the system to learn from limited data, transition fluidly between dialogue phases, and personalize responses to heterogeneous patient needs. We apply our framework to Motivational Interviews, aiming to foster behavior change, and demonstrate that the proposed dialogue manager outperforms a state-of-the-art LLM baseline in terms of reward, showing a potential benefit of conditioning LLMs to create open-ended dialogue systems with specific goals. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_19652 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tailored Conversations beyond LLMs: A RL-Based Dialogue Manager Galland, Lucie Pelachaud, Catherine Pecune, Florian Computation and Language Artificial Intelligence In this work, we propose a novel framework that integrates large language models (LLMs) with an RL-based dialogue manager for open-ended dialogue with a specific goal. By leveraging hierarchical reinforcement learning to model the structured phases of dialogue and employ meta-learning to enhance adaptability across diverse user profiles, our approach enhances adaptability and efficiency, enabling the system to learn from limited data, transition fluidly between dialogue phases, and personalize responses to heterogeneous patient needs. We apply our framework to Motivational Interviews, aiming to foster behavior change, and demonstrate that the proposed dialogue manager outperforms a state-of-the-art LLM baseline in terms of reward, showing a potential benefit of conditioning LLMs to create open-ended dialogue systems with specific goals. |
| title | Tailored Conversations beyond LLMs: A RL-Based Dialogue Manager |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2506.19652 |