Researchers introduced YallaMorph, a new large-scale benchmark designed to evaluate Arabic morphological generation capabilities in large language models (LLMs). The benchmark covers over 600,000 entries across verbs, nouns, adjectives, their cliticized forms, and invalid configurations. Evaluations on multilingual and Arabic-oriented LLMs demonstrated that Arabic morphological generation remains challenging, particularly for cliticized, unseen, and morphologically rare forms. Why it matters: This benchmark provides a critical tool for advancing the development and accuracy of Arabic LLMs by directly addressing a core linguistic challenge for the language.
LAraBench introduces a benchmark for Arabic NLP and speech processing, evaluating LLMs like GPT-3.5-turbo, GPT-4, BLOOMZ, Jais-13b-chat, Whisper, and USM. The benchmark covers 33 tasks across 61 datasets, using zero-shot and few-shot learning techniques. Results show that SOTA models generally outperform LLMs in zero-shot settings, though larger LLMs with few-shot learning reduce the gap. Why it matters: This benchmark helps assess and improve the performance of LLMs on Arabic language tasks, highlighting areas where specialized models still excel.
The paper introduces ALPS (Arabic Linguistic & Pragmatic Suite), a diagnostic challenge set for evaluating deep semantics and pragmatics in Arabic NLP. The dataset contains 531 expert-curated questions across 15 tasks and 47 subtasks, designed to test morpho-syntactic dependencies and compositional semantics. Evaluation of 23 models, including commercial, open-source, and Arabic-native models, reveals that models struggle with fundamental morpho-syntactic dependencies, especially those reliant on diacritics. Why it matters: ALPS provides a valuable benchmark for evaluating the linguistic competence of Arabic NLP models, highlighting areas where current models fall short despite achieving high fluency.
This survey paper analyzes over 40 benchmarks used to evaluate Arabic large language models, categorizing them into Knowledge, NLP Tasks, Culture and Dialects, and Target-Specific evaluations. It identifies progress in benchmark diversity but also highlights gaps like limited temporal evaluation and cultural misalignment. The paper also examines methods for creating benchmarks, including native collection, translation, and synthetic generation. Why it matters: The survey provides a comprehensive reference for Arabic NLP research and offers recommendations for future benchmark development to better align with cultural contexts.