TL;DR
STRIDE-ED is a novel framework for empathetic dialogue that models multi-stage reasoning with strategy-aware data refinement and training, improving response quality and emotional alignment.
Contribution
It introduces a comprehensive, interpretable reasoning framework with a data refinement pipeline and a two-stage training paradigm for empathetic dialogue systems.
Findings
Outperforms existing methods on automatic metrics.
Generalizes across diverse open-source LLMs.
Achieves better human evaluation scores.
Abstract
Empathetic dialogue requires not only recognizing a user's emotional state but also making strategy-aware, context-sensitive decisions throughout response generation. However, the lack of a comprehensive empathy strategy framework, explicit task-aligned multi-stage reasoning, and high-quality strategy-aware data fundamentally limits existing approaches, preventing them from effectively modeling empathetic dialogue as a complex, multi-stage cognitive and decision-making process. To address these challenges, we propose STRIDE-ED, a STRategy-grounded, Interpretable, and DEep reasoning framework that models Empathetic Dialogue through structured, strategy-conditioned reasoning. To support effective learning, we develop a strategy-aware data refinement pipeline integrating LLM-based annotation, multi-model consistency-weighted evaluation, and dynamic sampling to construct high-quality…
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