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New IslamicMMLU benchmark evaluates LLMs on Quran, Hadith, and Fiqh

By PulseAugur Editorial · [1 sources] ·

Researchers have developed IslamicMMLU, a new benchmark designed to evaluate the performance of large language models on core Islamic knowledge across three disciplines: Quran, Hadith, and Fiqh. The benchmark comprises over 10,000 multiple-choice questions and was used to assess 26 LLMs, with accuracy scores ranging from 39.8% to 93.8%. Notably, Gemini 3 Flash achieved the highest average accuracy, while Arabic-specific models showed mixed results and generally underperformed compared to frontier models. The benchmark also includes a task to detect bias related to different Islamic jurisprudence schools. AI

IMPACT This benchmark will enable more precise evaluation of LLMs' capabilities in understanding and responding to Islamic queries, potentially leading to improved models for religious and cultural contexts.

RANK_REASON The cluster describes a new academic benchmark for evaluating LLMs on a specific domain of knowledge. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New IslamicMMLU benchmark evaluates LLMs on Quran, Hadith, and Fiqh

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The cluster describes a new academic benchmark for evaluating LLMs on a specific domain of knowledge. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Ali Abdelaal, Mohammed Nader Al Haffar, Mahmoud Fawzi, Walid Magdy ·

    IslamicMMLU: A Benchmark for Evaluating LLMs on Islamic Knowledge

    arXiv:2603.23750v3 Announce Type: replace Abstract: Large language models are increasingly consulted for Islamic knowledge, yet no comprehensive benchmark evaluates their performance across core Islamic disciplines. We introduce IslamicMMLU, a benchmark of 10,013 multiple-choice …

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