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The paper introduces CARE, a GenAI framework that fine-tunes open-source LLMs for Hebrew and Arabic using counselor-validated crisis conversations to assist mental health counselors. CARE is trained on complete, highly-rated conversation histories to capture the emotional context and dynamics of effective de-escalation. Experiments show CARE achieves stronger semantic and strategic alignment with expert counselor responses than non-specialized LLMs, suggesting improved care quality in low-resource language settings.
Fine-tuning LLMs on expert-validated, real-world crisis conversations allows them to generate psychologically aligned responses that better support mental health counselors, even in low-resource languages.
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.
