研究新加坡俚语十年演变，发现大模型难以同时保持真实与时间中立。
Stylistic Evolution and LLM Neutrality in Singlish Language
- 通过词汇、语用等多维度分析俚语随时间变化规律。
- 多数大模型无法兼顾生成内容的真实性和时间中立性。
- 揭示了语言风格演化对模型输出的深层影响，适合语言技术研究者参考。
Singlish 是一种根植于新加坡多语言环境的克里奥尔语，持续随社会和技术变革而演进。本文基于十年间非正式数字消息中的语料，考察其历时性风格变化，并探讨大型语言模型（LLMs）能否生成时间中立的输出，以逼近该语言变体的稳定本质。利用词汇、语用、心理语言学及编码器特征，我们发现风格可分性随时间距离增加而上升，主要由长度和复杂度等结构特征驱动。与零假设分布基线对比，大多数 LLM 无法同时实现真实性与时间中立性，揭示出结构性权衡：生成逼真 Singlish 的模型会继承其时间偏差，而时间中立的模型则产出不真实的内容。这些发现将时间中立性定位为评估大模型社会方言扎根程度的诊断指标。
原文摘要 · Abstract (English)
Singlish is a creole rooted in Singapore's multilingual environment that continues to evolve alongside social and technological change. We examine diachronic stylistic change across a decade of informal digital messages and ask whether Large Language Models (LLMs) can generate temporally neutral outputs approximating the stable essence of the variety. Using lexical, pragmatic, psycholinguistic, and encoder-based features, we find that stylistic separability increases with temporal distance, driven primarily by structural features such as length and complexity. Evaluated against a null distribution baseline, most LLMs fail to achieve both authenticity and temporal neutrality simultaneously, revealing a structural trade-off: models generating realistic Singlish inherit its temporal biases, while temporally neutral models produce inauthentic outputs. These findings position temporal neutrality as a diagnostic metric for assessing sociolectal grounding in LLMs.
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