A r GAN : A rabic Gender, Ability, and Nationality Dataset for Evaluating Biases in Large Language Models (2025)

Abstract: Large language models (LLMs) are pretrained on substantial, unfiltered corpora, assembled from a variety of sources. This risks inheriting the deep-rooted biases that exist within them, both implicit and explicit. This is even more apparent in low-resource languages, where corpora may be prioritized by quantity over quality, potentially leading to more unchecked biases. More specifically, we address the biases present in the Arabic language in both general-purpose and Arabic-specialized architectures in three dimensions of demographics: gender, ability, and nationality. To properly assess the fairness of these models, we experiment with bias-revealing prompts and estimate the performance using existing evaluation metrics, and propose adaptations to others.

## introduction state-of-the-art large language models (llms) have had incredible progress in the current decade primarily due to the extremely large number of corpora used for training them. this leads to issues of fairness; data that contains underlined biases against certain demographics leads to prejudiced and biased results. @xcite . we aim to provide a thorough evaluation of the biases present in state-of-the-art llms. we focus on three demographic axes, namely gender, ability, and nationality, which are common real-world prejudice axes. the goal is to create effective prompts to reveal these biases in models. we also aim to evaluate these biases using current metrics and propose improvements to existing evaluation methods. we choose to work exclusively on the arabic language, and, more specifically, modern standard arabic (msa), within both generalpurpose llms and arabic-centric models. bias and toxicity research for arabic is underdeveloped due to its linguistic complexity, dialectal diversity, and gendered nature, leading to undetected biases. addressing these challenges is crucial for accurate evaluation in arabic nlp. we introduce argan: a dataset for evaluating biases in large language models in msa for gender, ability, and nationality. prompts were created with the purpose of extracting stereotypical, biased, and often toxic responses from the models. to work with the prompts, we create a dataset of aides curated for each demographic in the form of templates and descriptors, further discussed in the following section. related work bias is typically defined as skewed model outputs, which result from the presence of a particular identity or societal group in the input. the output usually contains common cultural stereotypes and more often than not they are toxic and offensive to the targeted group. previous work @xcite focused on uncovering these biases using templatebased datasets like the holisticbias dataset. one main limitation to similar works is focusing on resource-rich languages like english. lowresource languages like arabic have been marginalized due to a lack of data and benchmarking techniques. similar work focused more on cultural bias @xcite . works that focused on gender bias either didn't evaluate widely used, general-purpose, multilingual models (al qadi, 2023) or mainly focused on detecting gender biases in machine translation task @xcite and @xcite . as such, work on nationality and ability biases have been non-existent and marginalized in arabic bias identification. ## methodology our dataset contains a set of 20 templates and 125 nouns & adjectives from which resulted a total of 711 sentences (211 gender, 247 ability, and 253 nationality) to conduct our experiments, and it can be expanded to create even more. we designed prompts tailored for each template type. these prompts follow the same guidelines, seen in appendix b, but are adapted for each template as needed. the templates focus on stereotypes, misconceptions, and biased assumptions associated with each axis' demographics. in some instances, the model is asked to construct sentences based on descriptors, accompanied by roles or adjectives. in that case, it is explicitly told to use each of the given words precisely once to construct the sentences using the given template. to conduct our analysis, we choose three wellknown general-purpose, powerful llms: llama 3.3 @xcite , deepseek v3 @xcite , and gpt-4o @xcite . as well as two models primarily trained on arabic corpora: aya @xcite , well known for its high performance with arabic tasks and jais @xcite , a dedicated arabic-centric foundation model. ## results to assess the behaviour of the models across different axes, we used several metrics. it should be noted that not all metrics worked with all axes as the nature of the outputs tended to differ from one demographic to another. initially, gender bias was evaluated using three metrics: gender bias score, gpt bias, and relative difference in cosine similarity as a secondary metric. these metrics were particularly useful when it came to gender; as gender bias primarily manifested through stereotypical associations with both genders rather than through negative and derogatory outputs. both the nationality and ability axes were evaluated using regard, toxicity score, and identity attack metrics, as sentences in both of these axes tend to be more negative and offensive towards the targeted groups. ## conclusion in this study, we introduce argan, a dataset designed to evaluate bias in arabic large language models across gender, ability and nationality axes, aimed at addressing bias perpetuated by low-quality arabic data resources. employing a suite of evaluative tools, the study examined intrinsic, extrinsic bias and offense directed to certain demographic groups. our findings indicate that aya showed the least gender bias, while deepseek scored the highest bias across that axis, whereas llama outperformed its peers on both the ability and nationality axes, while gpt-4o exhibited the highest bias scores for nationality. overall, model performance varied notably across axes and metrics, with no single model consistently outperforming others.