In the area where Large Language Models (LLMs) are becoming an integral part of our daily lives, whether by shaping our digital interactions or helping in complex tasks, understanding their multilingual capabilities is paramount. These LLMs are trained using an extensive dataset composed of multiple languages and are being used extensively by users worldwide. This paper aims to evaluate the output quality of LLMs across diverse languages, focusing particularly on those with distinct linguistic characteristics, such as English and Albanian. Our comprehensive analysis explores the models’ proficiency in addressing problem statements, employing a combination of human evaluation and quantitative metrics. By prompting math problems, code tasks, and exploring sensitive questions, we assess the accuracy of responses in a coherent chain-of-thought manner. The objective is to unveil potential challenges associated with the multilingual functionalities of various LLMs, offering valuable insights into their linguistic adaptability and paving the way for future improvements in achieving robust language understanding and generation. This research represents a significant stride toward enhancing the applicability of language models in diverse linguistic contexts and deepening our comprehension of their capabilities.
Large Language Models, Artificial Intelligence, multilingual, text analysis, Natural Language Processing (NLP).
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