CARE：面向在线心理健康支持的咨询师对齐响应引擎
CARE: Counselor-Aligned Response Engine for Online Mental-Health Support
AI总结：
CARE通过针对希伯来语和阿拉伯语的微调，提升心理健康领域低资源语言下的响应质量，增强咨询师与求助者对话的动态结构和情感语境。
摘要：
全球心理健康挑战日益加剧，情绪支持服务面临压力，导致咨询师超负荷。在危机情况（如自杀念头）中，及时干预至关重要。尽管大语言模型（LLMs）在生成能力方面表现优异，但其在低资源语言和敏感领域（如心理健康）的应用仍显不足。现有基于LLM的代理往往难以复制专业人员使用的支持性语言和干预策略，因缺乏大规模真实世界数据的训练。为此，我们提出CARE（Counselor-Aligned Response Engine），一种GenAI框架，通过微调开源LLMs生成实时、心理对齐的响应建议。CARE分别针对希伯来语和阿拉伯语使用精心挑选的真实危机对话子集进行微调。训练数据由专业咨询师评定为高度有效的会话组成，使模型能捕捉成功缓和的互动模式。通过训练完整对话历史，CARE维持了咨询师-求助者对话的演变情感语境和动态结构。在实验设置中，CARE在语义和策略上比非专业LLM更接近金牌标准咨询师响应。这些发现表明，针对专家验证数据的领域特定微调可以显著支持咨询师工作流程，并在低资源语言环境中提高护理质量。
英文摘要：
Mental health challenges are increasing worldwide, straining emotional support services and leading to counselor overload. This can result in delayed responses during critical situations, such as suicidal ideation, where timely intervention is essential. While large language models (LLMs) have shown strong generative capabilities, their application in low-resource languages, especially in sensitive domains like mental health, remains underexplored. Furthermore, existing LLM-based agents often struggle to replicate the supportive language and intervention strategies used by professionals due to a lack of training on large-scale, real-world datasets. To address this, we propose CARE (Counselor-Aligned Response Engine), a GenAI framework that assists counselors by generating real-time, psychologically aligned response recommendations. CARE fine-tunes open-source LLMs separately for Hebrew and Arabic using curated subsets of real-world crisis conversations. The training data consists of sessions rated as highly effective by professional counselors, enabling the models to capture interaction patterns associated with successful de-escalation. By training on complete conversation histories, CARE maintains the evolving emotional context and dynamic structure of counselor-help-seeker dialogue. In experimental settings, CARE demonstrates stronger semantic and strategic alignment with gold-standard counselor responses compared to non-specialized LLMs. These findings suggest that domain-specific fine-tuning on expert-validated data can significantly support counselor workflows and improve care quality in low-resource language contexts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
