View a PDF of the paper titled Evaluating Arabic Large Language Models: A Survey of Benchmarks, Methods, and Gaps, by Ahmed Alzubaidi and 7 other authors.
Abstract: This survey provides the first systematic review of Arabic LLM benchmarks, analyzing 40+ evaluation benchmarks across NLP tasks, knowledge domains, cultural understanding, and specialized capabilities. We propose a taxonomy organizing benchmarks into four categories: Knowledge, NLP Tasks, Culture and Dialects, and Target-Specific Evaluations. Our analysis reveals significant progress in benchmark diversity while identifying critical gaps: limited temporal evaluation, insufficient multi-turn dialogue assessment, and cultural misalignment in translated datasets. We examine three primary approaches: native collection, translation, and synthetic generation, discussing their trade-offs regarding authenticity, scale, and cost. This work serves as a comprehensive reference for Arabic NLP researchers, providing insights into benchmark methodologies, reproducibility standards, and evaluation metrics while offering recommendations for future development.
Submission History
From: Basma El Amel Boussaha [view email]
[v1] Wed, 15 Oct 2025 11:25:33 UTC (132 KB)
[v2] Thu, 16 Oct 2025 12:22:13 UTC (145 KB)
Understanding Arabic Large Language Models
Arabic Large Language Models (LLMs) represent a significant area of research, particularly as the demand for Arabic natural language processing (NLP) technologies grows. This paper, "Evaluating Arabic Large Language Models: A Survey of Benchmarks, Methods, and Gaps," authored by Ahmed Alzubaidi and colleagues, tackles the essential benchmarks that are critical for evaluating the performance of Arabic LLMs.
The Significance of Benchmarks
Benchmarks serve as the cornerstone of any evaluation framework, providing standardized measures against which models can be assessed. Given the linguistic diversity and cultural richness of the Arabic language, establishing effective benchmarks is even more crucial. The authors identify over 40 distinct evaluation benchmarks that span various NLP tasks, knowledge domains, and cultural understanding. By categorizing these benchmarks, they provide a structured approach that aids researchers in comprehensively navigating the complex landscape of Arabic LLMs.
Proposed Taxonomy of Evaluation Benchmarks
One of the paper’s standout contributions is its proposed taxonomy, which organizes evaluation benchmarks into four key categories:
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Knowledge: This category assesses a model’s grasp of factual information and its ability to retrieve and generate knowledge-based responses.
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NLP Tasks: Covering various traditional NLP tasks such as sentiment analysis, named entity recognition, and translation, this category ensures models can handle a variety of language processing needs.
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Culture and Dialects: Recognizing that the Arabic-speaking community is diverse, benchmarks in this category evaluate a model’s ability to understand cultural nuances and dialect variations.
- Target-Specific Evaluations: These assessments focus on specific applications or sectors, discerning how models perform in specialized contexts.
Identified Gaps in Current Benchmarks
Despite the advancements highlighted in the survey, the authors pinpoint several critical gaps. One significant concern is the lack of temporal evaluations, which assess how LLMs adapt or perform over time. Similarly, the insufficient assessment of multi-turn dialogue interactions poses challenges. Effective conversational models need to maintain context over extended exchanges, a feature that current benchmarks often overlook. Additionally, cultural misalignment in translated datasets creates pitfalls for LLMs, highlighting the need for better-aligned training and evaluation resources.
Approaches to Benchmark Development
The paper examines three primary methodologies for collecting benchmarks:
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Native Collection: This involves gathering data directly from Arabic speakers, ensuring authenticity but often requiring time and resources.
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Translation: Leveraging existing benchmarks from other languages can be efficient, but it risks losing nuances specific to the Arabic language and culture.
- Synthetic Generation: Utilizing generative models to create benchmarks can scale evaluations quickly; however, this raises authenticity concerns.
Each approach has its trade-offs, and the authors discuss how to balance these concerning authenticity, scale, and cost.
Best Practices for Future Development
This survey acts as a foundational resource for Arabic NLP researchers by offering insights into evaluation metrics, reproducibility standards, and methodology improvements. By addressing the highlighted gaps and considering the recommended best practices, the community can pave the way for more reliable and effective Arabic LLMs.
In summary, "Evaluating Arabic Large Language Models" serves as a vital resource that provides clarity in the arena of Arabic NLP benchmarks. For researchers and developers aiming to enhance Arabic language technologies, this comprehensive analysis is not just informative but also transformative in shaping future benchmarks and methodologies that are critical for the evolution of Arabic LLMs.
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