CARE: Counselor-Aligned Response Engine for Online Mental-Health Support

TL;DR

CARE is a specialized AI framework that fine-tunes open-source language models to generate real-time, psychologically aligned response suggestions for counselors in low-resource languages like Hebrew and Arabic, enhancing mental health support quality.

Contribution

It introduces a domain-specific fine-tuning approach on real-world crisis data for LLMs to improve mental health response generation in low-resource languages.

Findings

CARE outperforms non-specialized LLMs in semantic and strategic alignment.

Models trained on expert-validated data better capture effective de-escalation patterns.

Fine-tuning on complete conversation histories preserves emotional context.

Abstract

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.