A Comprehensive Evaluation of Large Language Models on Mental Illnesses in Arabic Context

TL;DR

This study evaluates the performance of eight large language models on Arabic mental health datasets, highlighting the importance of prompt engineering, language configuration, and few-shot learning for improving diagnostic accuracy in Arabic contexts.

Contribution

It provides a comprehensive analysis of LLMs in Arabic mental health applications, emphasizing prompt design, multilingual factors, and few-shot prompting to enhance model effectiveness.

Findings

Prompt engineering significantly improves LLM scores.

Model selection impacts diagnostic performance, with Phi-3.5 MoE excelling in accuracy.

Few-shot prompting notably boosts multi-class classification accuracy.

Abstract

Mental health disorders pose a growing public health concern in the Arab world, emphasizing the need for accessible diagnostic and intervention tools. Large language models (LLMs) offer a promising approach, but their application in Arabic contexts faces challenges including limited labeled datasets, linguistic complexity, and translation biases. This study comprehensively evaluates 8 LLMs, including general multi-lingual models, as well as bi-lingual ones, on diverse mental health datasets (such as AraDepSu, Dreaddit, MedMCQA), investigating the impact of prompt design, language configuration (native Arabic vs. translated English, and vice versa), and few-shot prompting on diagnostic performance. We find that prompt engineering significantly influences LLM scores mainly due to reduced instruction following, with our structured prompt outperforming a less structured variant on multi-class datasets, with an average difference of 14.5\%. While language influence on performance was modest, model selection proved crucial: Phi-3.5 MoE excelled in balanced accuracy, particularly for binary classification, while Mistral NeMo showed superior performance in mean absolute error for severity prediction tasks. Few-shot prompting consistently improved performance, with particularly substantial gains observed for GPT-4o Mini on multi-class classification, boosting accuracy by an average factor of 1.58. These findings underscore the importance of prompt optimization, multilingual analysis, and few-shot learning for developing culturally sensitive and effective LLM-based mental health tools for Arabic-speaking populations.