MELAC: Massive Evaluation of Large Language Models with Alignment of Culture in Persian Language
This addresses the problem of cultural and linguistic bias in LLM evaluations for Persian speakers and researchers, though it is incremental as it extends existing benchmarking approaches to a new domain.
The study tackled the lack of evaluation resources for large language models in non-Western languages and cultures by introducing 19 new datasets for Persian language and Iranian culture, and benchmarked 41 LLMs to bridge this gap.
As large language models (LLMs) become increasingly embedded in our daily lives, evaluating their quality and reliability across diverse contexts has become essential. While comprehensive benchmarks exist for assessing LLM performance in English, there remains a significant gap in evaluation resources for other languages. Moreover, because most LLMs are trained primarily on data rooted in European and American cultures, they often lack familiarity with non-Western cultural contexts. To address this limitation, our study focuses on the Persian language and Iranian culture. We introduce 19 new evaluation datasets specifically designed to assess LLMs on topics such as Iranian law, Persian grammar, Persian idioms, and university entrance exams. Using these datasets, we benchmarked 41 prominent LLMs, aiming to bridge the existing cultural and linguistic evaluation gap in the field.