Stylistic Evolution and LLM Neutrality in Singlish Language
For computational sociolinguistics, this work introduces temporal neutrality as a diagnostic metric for assessing sociolectal grounding in LLMs.
The study examines stylistic change in Singlish over a decade and evaluates whether LLMs can generate temporally neutral outputs. It finds that most LLMs fail to achieve both authenticity and temporal neutrality simultaneously, revealing a structural trade-off.
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.