Stylistic Evolution and LLM Neutrality in Singlish Language
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866911717467881472 |
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| author | Foo, Linus Tze En Ng, Weihan Angela Li, Wenkai Ng, Lynnette Hui Xian |
| author_facet | Foo, Linus Tze En Ng, Weihan Angela Li, Wenkai Ng, Lynnette Hui Xian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_06580 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Stylistic Evolution and LLM Neutrality in Singlish Language Foo, Linus Tze En Ng, Weihan Angela Li, Wenkai Ng, Lynnette Hui Xian Computation and Language 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. |
| title | Stylistic Evolution and LLM Neutrality in Singlish Language |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2601.06580 |