ALPS: A Diagnostic Challenge Set for Arabic Linguistic & Pragmatic Reasoning

• URL: http://arxiv.org/abs/2602.17054v1
• Date: Thu, 19 Feb 2026 03:51:37 GMT
• Title: ALPS: A Diagnostic Challenge Set for Arabic Linguistic & Pragmatic Reasoning
• Authors: Hussein S. Al-Olimat, Ahmad Alshareef,
• Abstract summary: ALPS (Arabic Linguistic & Pragmatic Suite) is a native, expert-curated diagnostic challenge set probing Deep Semantics and Pragmatics.<n> ALPS targets the depth of linguistic understanding through 531 rigorously crafted questions across 15 tasks and 47 subtasks.<n>We developed the dataset with deep expertise in Arabic linguistics, guaranteeing cultural authenticity and eliminating translation artifacts.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While recent Arabic NLP benchmarks focus on scale, they often rely on synthetic or translated data which may benefit from deeper linguistic verification. We introduce ALPS (Arabic Linguistic & Pragmatic Suite), a native, expert-curated diagnostic challenge set probing Deep Semantics and Pragmatics, capabilities that complement specialized large-scale benchmarks. While broad-coverage benchmarks prioritize scale and multi-task coverage, ALPS targets the depth of linguistic understanding through 531 rigorously crafted questions across 15 tasks and 47 subtasks. We developed the dataset with deep expertise in Arabic linguistics, guaranteeing cultural authenticity and eliminating translation artifacts. Evaluating 23 diverse models (commercial, open-source, and Arabic-native) against a single-pass human performance (avg. 84.6% accuracy) and an expert-adjudicated oracle (99.2%), we reveal a critical dissociation: models achieve high fluency but fail on fundamental morpho-syntactic dependencies, with elevated error rates on morpho-syntactic dependencies (36.5% across diacritics-reliant tasks) compared to compositional semantics. While top commercial models (Gemini-3-flash at 94.2%) surpass the average single human, a substantial gap persists between commercial giants and Arabic-native models, with the best Arabic-specific model (Jais-2-70B at 83.6%) approaching but not matching human performance.

Related papers

• 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)
• BALSAM: A Platform for Benchmarking Arabic Large Language Models [34.50348949235453]
  BALSAM is a comprehensive, community-driven benchmark aimed at advancing Arabic LLM development and evaluation.<n>It includes 78 NLP tasks from 14 broad categories, with 52K examples divided into 37K test and 15K development, and a centralized, transparent platform for blind evaluation.
  arXiv  Detail & Related papers  (2025-07-30T12:16:39Z)
• 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)
• KoBALT: Korean Benchmark For Advanced Linguistic Tasks [0.6971903955510721]
  KoBALT (Korean Benchmark for Advanced Linguistic Tasks) is a linguistically-motivated benchmark comprising 700 multiple-choice questions.<n>It is designed to advance the evaluation of large language models (LLMs) in Korean.<n>It introduces a suite of expert-curated, linguistically motivated questions with minimal n-gram overlap with standard Korean corpora.
  arXiv  Detail & Related papers  (2025-05-22T02:03:07Z)
• MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation [86.7047714187813]
  MMLU-ProX is a benchmark covering 29 languages, built on an English benchmark.<n>Each language version consists of 11,829 identical questions, enabling direct cross-linguistic comparisons.<n>To meet efficient evaluation needs, we provide a lite version containing 658 questions per language.
  arXiv  Detail & Related papers  (2025-03-13T15:59:20Z)
• LINGOLY: A Benchmark of Olympiad-Level Linguistic Reasoning Puzzles in Low-Resource and Extinct Languages [8.754506364968394]
  The LingOly benchmark is a novel benchmark for advanced reasoning abilities in large language models. We evaluate capabilities for in-context identification and generalisation of linguistic patterns in very low-resource or extinct languages. We assess performance with both direct accuracy and comparison to a no-context baseline to penalise memorisation.
  arXiv  Detail & Related papers  (2024-06-10T11:50:29Z)
• 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)
• SOUL: Towards Sentiment and Opinion Understanding of Language [96.74878032417054]
  We propose a new task called Sentiment and Opinion Understanding of Language (SOUL) SOUL aims to evaluate sentiment understanding through two subtasks: Review (RC) and Justification Generation (JG)
  arXiv  Detail & Related papers  (2023-10-27T06:48:48Z)
• No Language Left Behind: Scaling Human-Centered Machine Translation [69.28110770760506]
  We create datasets and models aimed at narrowing the performance gap between low and high-resource languages. We propose multiple architectural and training improvements to counteract overfitting while training on thousands of tasks. Our model achieves an improvement of 44% BLEU relative to the previous state-of-the-art.
  arXiv  Detail & Related papers  (2022-07-11T07:33:36Z)
• Learning to Learn Morphological Inflection for Resource-Poor Languages [105.11499402984482]
  We propose to cast the task of morphological inflection - mapping a lemma to an indicated inflected form - for resource-poor languages as a meta-learning problem. Treating each language as a separate task, we use data from high-resource source languages to learn a set of model parameters. Experiments with two model architectures on 29 target languages from 3 families show that our suggested approach outperforms all baselines.
  arXiv  Detail & Related papers  (2020-04-28T05:13:17Z)

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.