AI 中文总结
该研究开发了粤语与爱尔兰语ParGram树库，探究多语言LLMs在语法工程中的应用，发现其翻译表现差、跨语言抽象任务效果不佳，但可提供参考，凸显其潜力与专家分析的重要性。
AI 中文摘要
语法工程需要具备语言形式化和计算实现方面的专业知识，尤其是在平行语法项目中，需平衡跨语言一致性与特定语言属性。本文介绍平行语法（ParGram）项目中粤语与爱尔兰语树库的开发情况，该项目在抽象功能层面维持语言平行性。我们还研究使用多语言大语言模型（LLMs）支持语法工程的方法学潜力与局限性，聚焦粤语-爱尔兰语翻译及使用OpenAI的gpt-oss-120b模型生成形式句法结构的任务。结果显示，翻译表现总体不佳，且不受提示语语言的影响；在句法结构生成任务中，该模型产出了一些结构有意义的输出，但在需要跨语言抽象的任务中表现较差。不过，LLM生成的输出仍可通过提供替代分析思路、部分捕捉谓词-论元关系而具备一定参考价值。总体而言，我们的研究结果凸显了LLM在协作语法工程中的潜力与局限性，同时强调了专家驱动的分析与验证仍具重要性。
英文摘要
Grammar engineering requires expertise in linguistic formalism and computational implementation, especially in parallel grammar projects that balance cross-linguistic consistency with language-specific properties. This paper presents the development of Cantonese and Irish treebanks within the Parallel Grammar (ParGram) Project, where linguistic parallelism is maintained at an abstract functional level. We also investigate the methodological potential and limitations of using multilingual LLMs to support grammar engineering, focusing on Cantonese-Irish translation and the generation of formal syntactic structures using OpenAI's gpt-oss-120b model. The results show that translation performance was generally unsatisfactory and unaffected by prompt language. For syntactic structure generation, the model produced some structurally meaningful outputs, but performed poorly on tasks requiring cross-linguistic abstraction. Nonetheless, LLM-generated outputs may still offer some reference value by suggesting alternative analyses and (partially) capturing predicate-argument relations. Overall, our findings highlight both the potential and limitations of using LLMs in collaborative grammar engineering, while underscoring the continued importance of expert-driven analysis and verification.
