Current LLMs and English syntax terminology translation

We aim to examine the extent to which Large Language Models (LLMs), specifically ChatGPT-5 can translate English syntax terminology into Arabic. We collected 44 terms from generative syntax previous works, including books and journal articles, as well as from our experience in the field. These terms were translated by humans, and then by ChatGPT-5. We then analyzed and compared both translations. We used the analytical and comparative approach amalgamated with micro approach in our analysis. Findings unveil that ChatGPT still cannot translate the core syntax properties embedded in the terms under study involving several syntactic (and semantic) challenges: only 25% of ChatGPT translations were accurate, while 38.6% were inaccurate, and 36.4.% were partially correct, which we consider appropriate. Based on these findings, a set of actionable strategies were proposed, the most notable of which is finetuning LLMs on syntactic terminology in both languages to better LLMs’ working mechanism for accurate or at least appropriate translation.