From Standard English to Singlish: A Retrieval-Augmented Approach for Code-Switched Creole Generation in Large Language Models

ACL ARR 2026 January Submission5430 Authors

Keywords: Code-switching, Retrieval-augmented generation, Controllable text generation, Low-resource language varieties, Singaporean English (Singlish)

Abstract: Code-switching in contact varieties like Singaporean English (Singlish) challenges natural language generation due to limited parallel data and rapid lexical evolution. We propose a retrieval-augmented generation (RAG) framework that externalizes dialectal knowledge into a curated lexicon, enabling controlled lexical code-switching without fine-tuning. Our approach retrieves candidate Singlish expressions and guides generation through sparse lexical substitution. Human evaluation with 164 Singaporean participants found RAG and zero-shot prompting equally natural and appropriate. Automatic analyses reveal different transformation regimes: zero-shot prompting induces extensive paraphrasing (median 23 token edits), whereas RAG performs minimal substitutions (median 1 edit) with higher semantic preservation (mean cosine similarity 0.978 vs. 0.926). Our results demonstrate that externalizing code-switching into lexical resources enables control and auditability without sacrificing perceived quality, offering practical advantages for rapidly evolving contact varieties.

Paper Type: Short

Research Area: Low-resource Methods for NLP

Research Area Keywords: NLP in resource-constrained settings, code-switching, dialects and language varieties, less-resourced languages, human evaluation

Contribution Types: Approaches to low-resource settings

Languages Studied: English, Singaporean English (Singlish)

Submission Number: 5430