Benchmarking Frontier LLMs on Arabic Cultural and Sociolinguistic Knowledge: A Cross-Evaluation Framework with Human SME Ground Truth
arXiv:2607.00139v1 Announce Type: new Abstract: The cost of human expert evaluation is a principal bottleneck to deploying language models in specialized, high-stakes domains. This is particularly acute for Arabic sociolinguistic knowledge: credible grading requires not only linguistic fluency but deep cultural familiarity that cannot be approximated by surface-level metrics. We address this with a cross-evaluation framework instantiated on two underrepresented Arabic dialect communities: Egyptian and Iraqi Arabic. We contribute 103 validated prompt-rubric pairs (70 Egyptian, 33 Iraqi; 53 Cult
The increasing deployment of LLMs and the recognition of their limitations in non-English, culturally nuanced contexts necessitate robust, specialized evaluation frameworks now.
This development is crucial for responsible and effective AI deployment in diverse linguistic and cultural domains, especially in high-stakes applications.
The ability to accurately benchmark and improve LLMs for underrepresented languages and cultures moves from theoretical aspiration to a concrete methodology, accelerating their utility beyond dominant Western contexts.
- · AI developers in the Arab world
- · Organizations deploying LLMs for Arabic-speaking populations
- · Researchers focused on sociolinguistics and AI ethics
- · Monolingual/monocultural LLMs
- · AI solutions lacking cultural sensitivity
- · Organizations relying solely on generic benchmarks
Improved performance and reliability of LLMs in Arabic cultural and sociolinguistic contexts.
Increased adoption and trust in AI systems by Arabic-speaking communities, fostering new applications and markets.
Potential for similar robust cross-evaluation frameworks to be developed for other underrepresented languages and cultures, leading to a more globally inclusive AI ecosystem.
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Read at arXiv cs.CL