This paper presents HamRaz, a novel Persian-language mental health dataset designed for Person-Centered Therapy (PCT) using Large Language Models (LLMs). Despite the growing application of LLMs in AI-driven psychological counseling, existing datasets predominantly focus on Western and East Asian contexts, overlooking cultural and linguistic nuances essential for effective Persian-language therapy. To address this gap, HamRaz combines script-based dialogues with adaptive LLM role-playing, ensuring coherent and dynamic therapy interactions. We also introduce HamRazEval, a dual evaluation framework that measures conversational quality and therapeutic effectiveness using General Dialogue Metrics and the Barrett-Lennard Relationship Inventory (BLRI). Experimental results show HamRaz outperforms conventional Script Mode and Two-Agent Mode, producing more empathetic, context-aware, and realistic therapy sessions. By releasing HamRaz, we contribute a culturally adapted, LLM-driven resource to advance AI-powered psychotherapy research in diverse communities.
View on arXiv@article{abbasi2025_2502.05982,
title={ HamRaz: A Culture-Based Persian Conversation Dataset for Person-Centered Therapy Using LLM Agents },
author={ Mohammad Amin Abbasi and Farnaz Sadat Mirnezami and Hassan Naderi },
journal={arXiv preprint arXiv:2502.05982},
year={ 2025 }
}