Large language models versus natural language processing in foreign languages

Abstract

Artificial Intelligence (AI) tools have become a port of our workflow when it comes to automating language processing tasks. AI language models are trained on massive datasets using deep learning algorithms to understand and generate human language. While these tools excel at capturing general patterns, they frequently fall short when it comes to less popular languages such as Romanian. This is largely because most models rely on publicly available text sources, books, websites, forums, that may not fully represent nuanced or region-specific usage. As a result, they can produce inaccurate outputs, especially when confronted with irregular grammar rules and linguistic exceptions. This article explores the limitations of current AI systems in accurately applying grammar rules, the role of high-quality data input, and possible strategies for addressing these challenges.