arXiv:2601.06580v2 Announce Type: replace 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 leng
This research is emerging as Large Language Models (LLMs) are becoming increasingly sophisticated and culturally embedded, leading to questions about their neutrality and influence on language evolution.
It highlights the potential for LLMs to either preserve or alter linguistic nuances, particularly in creoles like Singlish, which has implications for cultural identity and digital communication.
Understanding LLMs' generative patterns allows for assessment of their impact on language evolution and informs strategies for developing models that respect linguistic diversity and neutrality.
- · Linguists
- · Cultural preservation organizations
- · Developers of culturally sensitive AI
- · Homogeneous LLM developers
- · Users seeking authentic cultural expression
- · Monolingual AI services
LLM developers may begin integrating features to allow for more nuanced and temporally neutral language generation.
There could be a push for 'language-agnostic' or 'culturally-aware' AI training datasets and methodologies.
The development of 'culture-preserving AI' could become a new niche, impacting educational tools and national digital infrastructure.
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