Discourse particles, such as \textit{well} and \textit{kind of}, are crucial components that enable LLMs to ``speak'' more like humans. They are used to convey emotions, intentions, and interpersonal meanings. However, existing studies have not yet built a comprehensive understanding of LLMs' capabilities in handling discourse particles. Moreover, the limited number of studies focuses primarily on high-resource languages such as English, with little attention paid to Southeast Asian languages. In this paper, we (1) propose \textsc{MalayPrag}, a benchmark designed to systematically evaluate and analyze LLMs' capabilities in handling discourse particles in colloquial Malay; and (2) introduce five attributes that provide a linguistically grounded, unified framework for interpreting the pragmatic functions of discourse particles. Applying these two contributions, we prompt ten off-the-shelf LLMs to perform three prediction tasks. The experimental results reveal substantial challenges for current LLMs in accurately connecting discourse particles with their pragmatic functions in Malay. The provision of the five attributes designed in this study is found to significantly improve these connections, highlighting the need for structured scaffolding for models' pragmatic competence.

翻译：语气助词（如英语中的 \textit{well} 和 \textit{kind of}）是使大型语言模型（LLM）更接近人类表达方式的关键成分，用于传递情感、意图和人际意义。然而，现有研究尚未系统评估LLM处理语气助词的能力，且少量相关研究主要聚焦于英语等高资源语言，对东南亚语言关注不足。本文（1）提出 \textsc{MalayPrag} 基准测试集，用于系统评估和分析LLM处理马来语口语语气助词的能力；（2）引入五个语言属性，构建具有语言学基础、统一解释语气助词语用功能的框架。我们基于这两项贡献，促使十个现有LLM执行三项预测任务。实验结果表明，当前LLM在准确关联马来语气助词与其语用功能方面存在显著挑战。本研究所设计的五个属性可显著提升该关联能力，凸显了为模型语用能力提供结构化支撑的必要性。