As the use of large language models (LLMs) becomes increasingly global, understanding public attitudes toward these systems requires tools that are adapted to local contexts and languages. In the Arab world, LLM adoption has grown rapidly with both globally dominant platforms and regional ones like Fanar and Jais offering Arabic-specific solutions. This highlights the need for culturally and linguistically relevant scales to accurately measure attitudes toward LLMs in the region. Tools assessing attitudes toward artificial intelligence (AI) can provide a base for measuring attitudes specific to LLMs. The 5-item Attitudes Toward Artificial Intelligence (ATAI) scale, which measures two dimensions, the AI Fear and the AI Acceptance, has been recently adopted and adapted to develop new instruments in English using a sample from the UK: the Attitudes Toward General LLMs (AT-GLLM) and Attitudes Toward Primary LLM (AT-PLLM) scales. In this paper, we translate the two scales, AT-GLLM and AT-PLLM, and validate them using a sample of 249 Arabic-speaking adults. The results show that the scale, translated into Arabic, is a reliable and valid tool that can be used for the Arab population and language. Psychometric analyses confirmed a two-factor structure, strong measurement invariance across genders, and good internal reliability. The scales also demonstrated strong convergent and discriminant validity. Our scales will support research in a non-Western context, a much-needed effort to help draw a global picture of LLM perceptions, and will also facilitate localized research and policy-making in the Arab region.

翻译：随着大语言模型（LLMs）的全球应用日益广泛，理解公众对这些系统的态度需要适应当地语境和语言的评估工具。在阿拉伯世界，LLM的采用迅速增长，既有全球主导平台，也有如Fanar和Jais等提供阿拉伯语定制解决方案的区域性平台。这凸显了需要文化和语言相关量表来准确衡量该地区对LLM态度的必要性。评估人工智能（AI）态度的工具可为衡量LLM特定态度提供基础。包含五个项目、测量"AI恐惧"和"AI接受"两个维度的"人工智能态度（ATAI）量表"近期已被采用并改编，基于英国样本开发出英语新工具："通用大语言模型态度（AT-GLLM）量表"与"主要大语言模型态度（AT-PLLM）量表"。本文中，我们将AT-GLLM和AT-PLLM两个量表翻译成阿拉伯语，并使用249名阿拉伯语成年人的样本进行验证。结果表明，翻译成阿拉伯语的量表是可靠有效的工具，可用于阿拉伯人群和语言环境。心理测量学分析证实了双因子结构、跨性别的强测量不变性以及良好的内部信度。量表同时表现出较强的收敛效度和区分效度。我们的量表将支持非西方语境下的研究——这是绘制全球LLM认知图景亟需的努力，并将促进阿拉伯地区的本土化研究与政策制定。