Abstract: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.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2601.06580 [cs.CL] |
| (or arXiv:2601.06580v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2601.06580 arXiv-issued DOI via DataCite |
From: Linus Tze En Foo [view email]
[v1]
Sat, 10 Jan 2026 14:34:07 UTC (2,299 KB)
[v2]
Tue, 26 May 2026 04:23:35 UTC (166 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。