EPT Benchmark: Evaluation of Persian Trustworthiness in Large Language Models

Document Type : Research Article

Authors

Department of Computer Engineering, Sharif University of Technology, Tehran, Iran

10.22042/isecure.2026.242935
Abstract
Large Language Models (LLMs), trained on extensive datasets using advanced deeplearningarchitectures,havedemonstratedremarkableperformanceacrossa widerangeoflanguagetasks,becomingacornerstoneofmodernAItechnologies. However,ensuringtheirtrustworthinessremainsacriticalchallenge,asreliability is essential not only for accurate performance but also for upholding ethical, cultural, and social values. Careful alignment of training data and culturally grounded evaluation criteria is vital for developing responsible AI systems. In this study, we introduce the EPT (Evaluation of Persian Trustworthiness) metric, a culturally informed benchmark specifically designed to assess the trustworthiness of LLMs across six key aspects: Truthfulness, Safety, Fairness, Robustness, privacy, and ethical alignment. We curated a labelled dataset and evaluated the performance of several leading models—including ChatGPT, Claude, DeepSeek, Gemini, Grok, LLaMA, Mistral, and Qwen—using both automated LLM-based and human assessments. Our results reveal significant deficiencies in the safety dimension, underscoring the urgent need for focused attention on this critical aspect of model behaviour. Furthermore, our findings offer valuable insights into the alignment of these models with Persian ethical-cultural values and highlight critical gaps and opportunities for advancing trustworthy and culturally responsible AI. The dataset is publicly available at: https://github.com/Rezamirbagheri110/EPT-Benchmark.

Keywords


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Available Online from 01 January 2026