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Safe at the Margins: A General Approach to Safety Alignment in Low-Resource English Languages — A Singlish Case Study

Paper · 2025 · Isaac Lim, Shaun Khoo, Roy Ka-Wei Lee et al.

Summary

LLMs are mostly safety-tuned on English data, so they behave worse in creoles and code-mixed varieties. Lim et al. address this for Singlish, the English creole of Singapore. They take SEA-Lion-v2.1-Instruct, a Llama 3-8B model adapted to Southeast Asian languages but without explicit safety tuning, and compare three ways of steering it toward safe output: SFT, DPO and KTO. The training data comes from SGToxicityPrompts, which holds real benign and toxic Singlish posts from a local forum and Singapore subreddits. Each post is wrapped in conversational templates, and GPT-4o writes refusals for the unsafe ones.

Because KTO accepts single responses labelled good or bad, it can use the safe prompts that DPO must discard. SFT followed by KTO gave the best balance: near-total refusal of toxic requests, fewer false refusals on benign ones, and little loss on general benchmarks. The authors also propose KTO-S, a sign-corrected KL term that smooths training. The work offers a practical recipe for aligning models in data-poor dialect settings.

Key points

  • Base model: SEA-Lion-v2.1-Instruct (Llama 3-8B variant). Training data: SGToxicityPrompts, built from HardwareZone's Eat-Drink-Man-Woman forum and Singapore subreddits. The motivation includes the fact that only 3% of Llama 3's SFT data is non-English.
  • The authors wrote 21 prompt templates to vary user intent. After manual review, 10 were removed from the safe subset because they accidentally drew out unsafe replies.
  • Refusals were detected with distilroberta-base-rejection-v1, with prefix matching as a backstop. SFT+KTO reached a 99.6% refusal rate on SGToxicityPrompts and also generalized to TOXIGEN.
  • When KTO was restricted to the paired unsafe subset only, as DPO is, its false positive rate on benign content rose to 30.6%. SFT+DPO also showed a sharp FPR increase.
  • On Open LLM Leaderboard v2, average changes relative to the base model were −2.94% for SFT, +2.14% for KTO and −2.89% for SFT+KTO.

Interesting ideas

  • I1. Supervision can mix paired contrasts (good vs. bad answer) with lone examples labelled only as acceptable, so no data is wasted when pairs cannot be built.
  • I2. Always measure the cost on benign inputs (false positive rate) next to the target gain, because a method that looks better on the target can simply be over-triggering.
  • I3. Aligning on authentic dialect text rather than machine-translated English keeps code-mixed constructions that translation would smooth away.
  • I4. Template augmentation is followed by a manual audit that removes templates which produce unintended behaviour, rather than trusting all generated variants.
  • I5. Training was smoother when a plain supervised stage came first, and when the regularizer saturated according to the sign of the reward instead of a fixed batch reference.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (learn): silver parses agreed by several witness parsers could enter training as single "accepted" items, while disputed ones become contrast pairs with the teacher's correction.
  • I2 (learn): every new repair rule should be scored on a negative-control set where it must not fire, not only on the cases it fixes.
  • I3 (tree): UD dialect conversion and treebank work for nonstandard English should rely on genuine forum-style sentences, so that creole-specific particles and word order are actually represented.
  • I4 (answer): generated school-test or word-problem variants should pass a review step that drops templates whose rephrasings change the expected answer.
  • I5 (learn): when the GPU parser is fine-tuned with preference-style signals, warm-starting it with ordinary supervised training and tracking divergence from the reference model could prevent unstable runs.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksSocial Sciences · 1,954 worksSafety Research · 626 works

🧭 Topics 3Research subjects and languages the work is about.English · 2,379 worksBias, fairness & safety · 587 worksLow-resource languages · 287 works

Cite

Cite as Isaac Lim, Shaun Khoo, Roy Ka-Wei Lee, Watson Chua, Jia Yi Goh and Jessica Foo. 2025. Safe at the Margins: A General Approach to Safety Alignment in Low-Resource English Languages — A Singlish Case Study. arXiv:2502.12485.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2502.12485 · arXiv 2502.12485

@misc{lim2025safe,
  title = {{Safe at the Margins: A General Approach to Safety Alignment in Low-Resource English Languages — A Singlish Case Study}},
  author = {Isaac Lim, Shaun Khoo, Roy Ka-Wei Lee, Watson Chua, Jia Yi Goh and Jessica Foo},
  year = {2025},
  doi = {10.48550/arxiv.2502.12485},
  eprint = {2502.12485},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2502.12485}
}

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