Tailored Conversations beyond LLMs: A RL-Based Dialogue Manager

2025-06-24

AI Summary

Goal-oriented open-domain dialogue systems struggle to simultaneously achieve user personalization, phase adaptability, and low-data learning. Method: This paper proposes a novel framework integrating large language models (LLMs) with a hierarchical reinforcement learning (HRL)-based dialogue manager. Specifically: (i) HRL models multi-phase dialogue policies to enable smooth, goal-driven transitions across phases; (ii) a meta-learning mechanism enables rapid personalization across diverse user profiles; and (iii) an LLM–HRL co-architecture decouples semantic generation from policy decision-making, reducing reliance on annotated dialogue data. Results: Evaluated on motivational interviewing tasks, the proposed dialogue manager achieves significantly higher reward scores than state-of-the-art LLM-based baselines, demonstrating superior performance in goal completion rate, user adaptability, and data efficiency.

Technology Category

Machine Learning: Large Multimodal Models (LMMs)Data Mining & Knowledge Management: Conversational Systems for Recommendation & RetrievalNatural Language Processing: Conversational AI/Dialog Systems

Application Category

User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendationSemantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactionsSearch and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

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.

Problem

• Integrate LLMs with RL for goal-oriented open-ended dialogue
• Enhance adaptability using meta-learning for diverse user profiles
• Improve efficiency in behavior change Motivational Interviews

Innovation

• RL-based dialogue manager for goal-oriented conversations
• Hierarchical reinforcement learning for structured dialogue phases
• Meta-learning for adapting to diverse user profiles