Beyond Fluency: A Rubric-Based Benchmark for Evaluating Saudi Dialect and Cultural Competence in Large Language Models

• URL: http://arxiv.org/abs/2608.29990v1
• Date: Sun, 30 Aug 2026 19:31:35 GMT
• Title: Beyond Fluency: A Rubric-Based Benchmark for Evaluating Saudi Dialect and Cultural Competence in Large Language Models
• Abstract summary: We present a rubric-based benchmark for the Saudi dialect.<n>We catalogue 466 error instances under a nine-category taxonomy.<n>We observe a consistency-versus-ceiling trade-off and model-distinctive error signatures.
• Score: 0.37331950863394864
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models are increasingly deployed in Arabic-speaking markets, yet standard benchmarks overwhelmingly reward Modern Standard Arabic (MSA) fluency while leaving dialectal and culturally grounded competence unmeasured. This gap is consequential: everyday Arabic is largely dialectal, and dialect encodes social meaning that MSA-centric evaluation cannot capture. We present a rubric-based benchmark for the Saudi dialect, comprising 31 expert-authored prompts spanning idiomatic, pragmatic, lexical, and culturally-embedded phenomena, each paired with an expert-established ground truth. Our methodology separates evaluation into a model-agnostic phase, in which atomic, MECE positive criteria are derived solely from the ground truth, and a model-specific phase, in which four state-of-the-art systems -- Claude Opus 5, Gemini 3.7, GPT-5.6, and Kimi K3 -- are scored against those criteria and penalised for errors they actively introduce. Across 124 model-prompt evaluations we catalogue 466 error instances under a nine-category taxonomy. The four systems cluster within a narrow macro-average band (42.7%-53.1%), with no model exceeding 55% and every model recording at least one negative-scoring prompt, confirming that Saudi dialectal competence remains broadly unsolved. Notably, Ambiguous Framing is the dominant failure mode (37.3% of errors) while outright Hallucination accounts for only 11.2%, indicating that models fail less by stating falsehoods than by distorting register and flattening pragmatic nuance. We further observe a consistency-versus-ceiling trade-off and model-distinctive error signatures. We release the full prompt set, ground truths, and scored rubrics to support reproducible dialectal evaluation.

Related papers

• NovGauge: A Fine-Grained Benchmark for Diagnosing LLMs' Capability in Paper Novelty Assessment [54.4265627247104]
  Large language models (LLMs) are increasingly used in peer review at major AI conferences.<n>Existing benchmarks assess novelty as a single holistic score.<n>We present NovGauge, a human-anchored benchmark for fine-grained novelty assessment diagnosis.
  arXiv  Detail & Related papers  (2026-09-10T08:34:56Z)
• Benchmarking Frontier LLMs on Arabic Cultural and Sociolinguistic Knowledge: A Cross-Evaluation Framework with Human SME Ground Truth [0.34797121357690153]
  Cross-evaluation framework instantiated on two underrepresented Arabic dialect communities: Egyptian and Iraqi Arabic.<n>We contribute 103 validated prompt-rubric pairs authored and graded by native-speaker SMEs.<n>Across 1,307 judge evaluations: GPT-5.4 is the most reliable judge (MADj = 10.21 pp, Signed Error = -1.12%); four of five judges show systematic leniency (+2.01% to +6.56%)
  arXiv  Detail & Related papers  (2026-06-30T20:18:32Z)
• Evaluation of Small Language Models for Arabic Language Processing [0.0]
  The study introduces a benchmark of 240 Arabic test items distributed across eight domains and ten language skills.<n>All models were evaluated under a controlled zero-shot setting using a standardized Arabic-only prompt template.<n>Models with stronger Arabic alignment and more reliable instruction-following behavior tended to perform better across tasks.
  arXiv  Detail & Related papers  (2026-06-19T14:16:13Z)
• Perception or Prejudice: Can MLLMs Go Beyond First Impressions of Personality? [74.69723255239663]
  Grounded Personality Reasoning requires MLLMs to anchor each Big Five rating in observable evidence through a chain of rating, reasoning, and grounding.<n> MM-OCEAN is produced by a multi-agent pipeline with human verification, with timestamped behavioral observations, evidence-grounded trait analyses, and seven categories of cue-grounding MCQs.<n>The analysis uncovers a striking Prejudice Gap: across the field, 51% of correct ratings are not grounded in retrieved cues, and the Holistic-Grounding Rate spans only 0-33.5%.
  arXiv  Detail & Related papers  (2026-05-21T07:42:47Z)
• Context-Aware Dialectal Arabic Machine Translation with Interactive Region and Register Selection [0.0]
  Current Machine Translation (MT) systems for Arabic often struggle to account for dialectal diversity.<n>We propose a context-aware framework for dialectal Arabic MT that explicitly models regional andlinguistic variation.
  arXiv  Detail & Related papers  (2026-04-07T20:54:19Z)
• DialectalArabicMMLU: Benchmarking Dialectal Capabilities in Arabic and Multilingual Language Models [54.10223256792762]
  We present DialectalArabicMMLU, a new benchmark for evaluating the performance of large language models (LLMs) across Arabic dialects.<n>We extend the MMLU-Redux framework through manual translation and adaptation of 3K multiple-choice question-answer pairs into five major dialects.
  arXiv  Detail & Related papers  (2025-10-31T15:17:06Z)
• DialectGen: Benchmarking and Improving Dialect Robustness in Multimodal Generation [111.94720088481614]
  Can multimodal generative models effectively produce content given dialectal textual input?<n>We construct a new large-scale benchmark spanning six common English dialects.<n>We design a general encoder-based mitigation strategy for multimodal generative models.
  arXiv  Detail & Related papers  (2025-10-16T17:56:55Z)
• UI-Level Evaluation of ALLaM 34B: Measuring an Arabic-Centric LLM via HUMAIN Chat [1.2788586581322734]
  Saudi Data and AI Authority introduced the $ALLaM$ family of Arabic-focused models.<n>The most capable of these available to the public, $ALLaM-34B$, was adopted by HUMAIN, who developed and deployed HUMAIN Chat.<n>This paper presents an expanded and refined UI-level evaluation of $ALLaM-34B$.
  arXiv  Detail & Related papers  (2025-08-24T14:32:15Z)
• Advancing Dialectal Arabic to Modern Standard Arabic Machine Translation [22.369277951685234]
  This paper presents two core contributions to advancing DA-MSA translation for the Levantine, Egyptian, and Gulf dialects.<n>Few-shot prompting consistently outperformed zero-shot, chain-of-thought, and our proposed Ara-TEaR method.<n>For fine-tuning LLMs, a quantized Gemma2-9B model achieved a chrF++ score of 49.88, outperforming zero-shot GPT-4o (44.58)
  arXiv  Detail & Related papers  (2025-07-27T14:37:53Z)
• ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic [51.922112625469836]
  We present datasetname, the first multi-task language understanding benchmark for the Arabic language. Our data comprises 40 tasks and 14,575 multiple-choice questions in Modern Standard Arabic (MSA) and is carefully constructed by collaborating with native speakers in the region. Our evaluations of 35 models reveal substantial room for improvement, particularly among the best open-source models.
  arXiv  Detail & Related papers  (2024-02-20T09:07:41Z)
• Holistic Evaluation of Language Models [183.94891340168175]
  Language models (LMs) are becoming the foundation for almost all major language technologies, but their capabilities, limitations, and risks are not well understood. We present Holistic Evaluation of Language Models (HELM) to improve the transparency of language models.
  arXiv  Detail & Related papers  (2022-11-16T18:51:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.