用角色扮演框架训练出能精准回应情绪需求的聊天助手。
SweetieChat: A Strategy-Enhanced Role-playing Framework for Diverse Scenarios Handling Emotional Support Agent
- 三角色模拟真实对话：求助者、策略顾问、支持者协同生成多样回复。
- 构建3.7K+多轮对话数据集，支持开放场景下情绪响应。
- 相比传统模型更自然细腻，适合心理咨询等高要求场景。
大型语言模型在提供共情支持方面展现出巨大潜力，但其回应常冗长或刻板，难以满足真实场景中多样化的情感需求。为此，我们提出一种增强型角色扮演框架，通过两步实现：(1) 策略增强的角色扮演交互，引入求助者、策略顾问和支援者三个关键角色，在多种情境下模拟真实互动，拓展对话多样性；(2) 情绪支持代理训练，基于自建数据集对大模型进行微调。该框架构建了名为ServeForEmo的数据集，包含超过3.7K个多轮对话和62.8K条语句。进一步提出了SweetieChat情绪支持代理，可处理开放域多样场景。大量实验与人工评估验证了该框架在提升情感支持效果方面的有效性，展现出更细腻、个性化的响应能力。
原文摘要 · Abstract (English)
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.
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