PersuHMM: Iterative Learning the Hierarchical Meta-Strategy Memory for Persuasive Dialogue

Abstract

Persuasive dialogue aims to alter people's attitudes or behaviors through conversation. While LLMs can generate emotional responses, they tend to use a uniform approach across varied scenarios, limiting their persuasive effectiveness. Diverse demands need varied persuasion strategies, which challenge both LLMs and human efforts. To address this, this paper incorporates the idea of meta-learning and proposes PersuHMM, an iterative learning approach for constructing the Hierarchical Meta-strategy Memory to generate Persuasive dialogue. Through the interaction between Meta‑layer and Task‑layer, the approach continuously accumulates automated meta‑strategies from generated outcomes and data labels, forming a persuasive meta‑strategy memory that balances efficiency and generalizability. To facilitate model retrieval and use, the meta-strategy memory is hierarchically organized via clustering for a clearer and more logical storage structure. Experimental results show that the hierarchical meta-strategy memory enhances both the persuasiveness and generalizability of the persuasion model, and even enables cross-model performance improvement.