Arabic LLM evaluation before you sign implementation.
Early 2026: a regulated director opened an empty RACI sheet and a public model comparison page; the next week delivered two conflicting spreadsheets because nobody recorded they measured different workloads.
This is procurement Arabic LLM evaluation in one sentence — bind performance to vault samples, not to internet polish [1]. At Nuqta we freeze gates before GPUs or sprawling API tiers expand.
Why Gulf buyers need their own corpus.
Finance and legal care about clauses, numbering, bilingual tables, retention — none of those appear in sanitized blog benchmarks [2]. Align with broader risk framing [1][2].
Three tasks × two hundred rows.
Pick summarize / extract-field / scripted support answer; each yields two hundred rows with a gold label and named adjudication before contract ink [6]. Operational comparison lives in our Arabic GPT-4o vs Qwen sprint.
The board-facing metric is not the logo — it is two hundred truthful rows plus one threshold signed by someone who may halt production.
Stratified Arabic workloads.
Separate formal memoranda from Gulf conversational tickets and bilingual tables — blended averages disguise the failure vector compliance will care about first [3].
Put numbers in the appendix.
Lock at least one acceptance metric numerically — field match ratio, acceptable hallucination tier, SLA on human review queues — otherwise procurement debates brands, not obligations [4].
The invitation.
Pair these gates with failure patterns in why AI fails in MENA plus Omani contract clauses. If the one-page brief cannot be finished inside an hour, you already know where work starts.
Frequently asked questions.
- Demo vs procurement eval? Benchmarks chase screenshots; evaluations freeze your corpus and thresholds [6].
- Row counts? Aim for roughly two hundred per critical workload before trusting production rollout [6].
- Vendor cloud? Procurement still owes PDPL-aligned paths — revisit PDPL × AI primer.
- Arabic leaderboard enough? Crowdsourced taste ≠ regulated retention posture [2].
- Who adjudicates disagreements between legal and ML? Named approver on the RACI sheet before spend gets serious [6].
Sources.
[1] NIST — AI Risk Management Framework (AI RMF 1.0).
[2] OECD — OECD AI Principles.
[3] Manning, Raghavan, Schütze — Introduction to Information Retrieval (evaluation primitives).
[4] McKinsey — The State of AI.
[5] OpenAI — GPT-4 Technical Report.
[6] Nuqta — procurement eval brief covering Arabic workloads, GCC, June 2026.
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