## 1 Introduction  
Large language models (LLMs) have revolutionized natural language processing (NLP) and computing technologies with unprecedented potential for providing satisfactory answers to user inputs across different domains and in multiple languages. The ability of LLMs--such as the popular ChatGPT--to provide tangible answers to questions or even pass difficult examinations in various fields, including medicine [1, 2] and mathematics [3, 4], has amazed many and become a subject of investigation for others.  
The increasing popularity and wide acceptance of LLMs are based on their seeming and robustness built on their ability for self-awareness [5], self-learning [6, 7], and processing tasks without prior proper fine-tuning for such specific tasks (zero-shot) [8, 9]. This was, however, not always the case as these capabilities were not present in smaller models [10, 11]. Refs. [12, 13] submitted that LLMs find it hard to cope when dealing with tasks that require systematic and logical reasoning. Several models, such as the chain-of-thought (CoT) prompting method and zero-shot CoT [14], have been proposed to enhance such capabilities so that LLMs can properly handle arithmetic reasoning tasks [12]. Standing on the axiom that \"numbers rule the universe\", several research endeavors, including Refs. [15, 16], have underscored the necessity of reasoning with numbers and training AI systems to do the same. In 2022, [PERSON]. [17] submitted that optimal AI models are brittle at best while dealing with arithmetic reasoning tasks when presented in different formats. While the ability of LLMs to handle tasks requiring systematic reasoning for mathematical challenges has improved through several approaches [15; 18], most of the deductive reasoning tasks on which these models are benchmarked are in the English language.  
In this work, we seek to contribute to the assessment of LLMs by evaluating their capabilities in providing zero-shot responses to arithmetic reasoning tasks in low-resourced languages (LRL), Yoruba, in particular, using a dataset that has minimal contamination. Yoruba is one of the most common languages in West Africa, with more than 40 million speakers globally. We chose arithmetic reasoning tasks for this assessment because they present an opportunity to analyze LLM's ability to understand and make systematic deductions from LRLs. We also present a set of Yoruba numerical probes curated for this evaluation. The probes are made up of three categories of questions. The first set is made up of basic arithmetic comprising addition (isiro or aropo), subtraction (ayokuro), division (pipin), multiplication (isodipup), as well as a combination of these operations. The second set contains problems relating to time, day of the week, and month of the year, while the third set of questions deals with Yoruba numerals and the counting system (onka).  
To this end, we present the performance of three publicly available LLMs on our curated numerical probes and evaluate how the models perform in a zero-shot setup on the probes with minimal contamination. In zero-shot learning, a model is prompted to perform a task based on its internal knowledge without having been explicitly trained on examples related to that task [19]. While approaches such as few-shot learning or model fine-tuning can improve the performance of LLMs, we adopted a zero-shot approach to assess their inherent abilities and establish baseline capabilities without any augmentation or fine-tuning.  
Our contributions in this study include (i) the introduction of Yoruba numerical probes--the first, to the best of our knowledge--with minimal contamination, designed to evaluate the performance of LLMs on natural language understanding (NLU) and natural language reasoning (NLR) tasks for LRLs; (ii) an assessment of the NLR capabilities of three LLMs in a zero-shot setup using the same set of probes; (iii) identification of the strengths and weaknesses of these LLMs in NLU and NLR tasks for LRLs (Yoruba in this case).