We aim to examine the extent to which Large Language Models (LLMs) can 'talk much' about grammar modules, providing evidence from syntax core properties translated by ChatGPT 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 an analytical and comparative approach in our analysis. Findings unveil that LLMs still cannot 'talk much' about 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 a close collaboration between AI specialists and linguists to better LLMs' working mechanism for accurate or at least appropriate translation.

翻译：我们旨在考察大语言模型（LLMs）在何种程度上能够“充分谈论”语法模块，基于ChatGPT将句法核心属性翻译至阿拉伯语时提供的证据。我们从生成句法学的既有文献（包括书籍与期刊文章）及自身领域经验中收集了44个术语。这些术语先由人工翻译，随后由ChatGPT-5翻译。我们采用分析性与比较性方法对两组译文进行剖析与对比。研究结果表明，大语言模型仍无法“充分谈论”所涉术语中蕴含的核心句法属性，其过程面临多项句法与语义挑战：仅25%的ChatGPT译文完全准确，38.6%存在错误，36.4%部分正确（本研究中将其视为可接受译文）。基于上述发现，我们提出了一系列可行策略，其中最显著的是倡导人工智能专家与语言学家紧密协作，以优化大语言模型的工作机制，实现准确或至少可接受的翻译效果。