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arXiv 2604.03395cs.CL
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阿拉伯基准可靠吗?QIMMA的高质量LLM评估方法

Are Arabic Benchmarks Reliable? QIMMA's Quality-First Approach to LLM Evaluation

Technology Innovation Institute(技术创新研究所)

Leen AlQadi, Ahmed Alzubaidi, Mohammed Alyafeai, Hamza Alobeidli, Maitha Alhammadi, Shaikha Alsuwaidi, Omar Alkaabi, Basma El Amel Boussaha, Hakim Hacid

AI总结:

QIMMA通过多模型评估流程解决阿拉伯基准系统性质量问题,构建了包含52000多个样本的多领域多任务评估套件,为阿拉伯自然语言处理评估提供可复现的基础。

摘要:

我们提出了QIMMA,一个保证质量的阿拉伯LLM排行榜,其核心是系统性的基准验证。不同于直接汇总现有资源,QIMMA采用结合自动LLM判断和人工审查的多模型评估流程,以揭示并解决在已建立的阿拉伯基准中系统性的质量问题。结果是一个经过精心挑选、多领域、多任务的评估套件,包含超过52000个样本,主要基于原生阿拉伯内容;代码评估任务是唯一的例外,因为它们本质上是语言无关的。通过LightEval、EvalPlus实现透明实施,并公开每个样本的推理输出,使QIMMA成为阿拉伯NLP评估的可复现和社区可扩展的基础。

英文摘要:

We present QIMMA, a quality-assured Arabic LLM leaderboard that places systematic benchmark validation at its core. Rather than aggregating existing resources as-is, QIMMA applies a multi-model assessment pipeline combining automated LLM judgment with human review to surface and resolve systematic quality issues in well-established Arabic benchmarks before evaluation. The result is a curated, multi-domain, multi-task evaluation suite of over 52k samples, grounded predominantly in native Arabic content; code evaluation tasks are the sole exception, as they are inherently language-agnostic. Transparent implementation via LightEval, EvalPlus and public release of per-sample inference outputs make QIMMA a reproducible and community-extensible foundation for Arabic NLP evaluation.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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