An end-to-end RL framework that uses simulated future dialogue and a learned future-oriented reward model to fine-tune LLMs for open-ended emotional support, reporting improved success rates on ESConv and ExTES.
Convert Language Model into a Value-based Strategic Planner
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abstract
Emotional support conversation (ESC) aims to alleviate the emotional distress of individuals through effective conversations. Although large language models (LLMs) have obtained remarkable progress on ESC, most of these studies might not define the diagram from the state model perspective, therefore providing a suboptimal solution for long-term satisfaction. To address such an issue, we leverage the Q-learning on LLMs, and propose a framework called straQ*. Our framework allows a plug-and-play LLM to bootstrap the planning during ESC, determine the optimal strategy based on long-term returns, and finally guide the LLM to response. Substantial experiments on ESC datasets suggest that straQ* outperforms many baselines, including direct inference, self-refine, chain of thought, finetuning, and finite state machines.
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Towards Open-Ended Emotional Support Conversations in LLMs via Reinforcement Learning with Future-Oriented Rewards
An end-to-end RL framework that uses simulated future dialogue and a learned future-oriented reward model to fine-tune LLMs for open-ended emotional support, reporting improved success rates on ESConv and ExTES.