Large Language Models (LLMs) have demonstrated promising potential in providing empathetic support during interactions. However, their responses often become verbose or overly formulaic, failing to adequately address the diverse emotional support needs of real-world scenarios. To tackle this challenge, we propose an innovative strategy-enhanced role-playing framework, designed to simulate authentic emotional support conversations. Specifically, our approach unfolds in two steps: (1) Strategy-Enhanced Role-Playing Interactions, which involve three pivotal roles -- Seeker, Strategy Counselor, and Supporter -- engaging in diverse scenarios to emulate real-world interactions and promote a broader range of dialogues; and (2) Emotional Support Agent Training, achieved through fine-tuning LLMs using our specially constructed dataset. Within this framework, we develop the \textbf{ServeForEmo} dataset, comprising an extensive collection of 3.7K+ multi-turn dialogues and 62.8K+ utterances. We further present \textbf{SweetieChat}, an emotional support agent capable of handling diverse open-domain scenarios. Extensive experiments and human evaluations confirm the framework's effectiveness in enhancing emotional support, highlighting its unique ability to provide more nuanced and tailored assistance.

翻译：大型语言模型（LLMs）在交互过程中展现出了提供共情支持的潜力。然而，其回应往往变得冗长或过于公式化，难以充分满足现实场景中多样化的情感支持需求。为应对这一挑战，我们提出了一种创新的策略增强型角色扮演框架，旨在模拟真实的情感支持对话。具体而言，我们的方法分两步展开：（1）策略增强型角色扮演交互，涉及三个关键角色——求助者、策略顾问与支持者——在多样化场景中进行互动，以模拟现实世界对话并促进更广泛的交流；（2）情感支持智能体训练，通过使用我们专门构建的数据集对LLMs进行微调来实现。在此框架内，我们开发了 \textbf{ServeForEmo} 数据集，其中包含超过3.7K轮多轮对话和超过62.8K条话语的广泛集合。我们进一步提出了 \textbf{SweetieChat}，这是一个能够处理多样化开放域场景的情感支持智能体。大量实验和人工评估证实了该框架在增强情感支持方面的有效性，突显了其提供更细致、更个性化辅助的独特能力。