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The Multilingual Alignment Prism: Aligning Global and Local Preferences to Reduce Harm

Paper · 2024 · Aakanksha, Arash Ahmadian, Beyza Ermis et al.

Summary

Most LLM safety alignment targets English and relies on Western-centric data. This leaves non-English users more exposed and lets other languages serve as a route around guardrails. The paper asks three things: whether harm mitigation can work across many languages at once, whether it transfers across languages, and whether culturally specific ("local") harms need different handling from universally recognized ("global") ones. Paid native speakers wrote red-teaming prompts in eight languages. Each prompt carries harm categories and a global/local tag, and the set is released as the Aya Red-teaming dataset. The authors expanded seed prompts synthetically, built preference pairs from two multilingual models judged by an LLM, and combined these with a translated general-purpose preference set. They then compared SFT variants against DPO across 6 languages. Offline preference training substantially reduced harmful outputs while preserving or improving general generation quality, and DPO(SFT) offered the best balance. Training on one harm type also reduced the other. The work matters because it provides a rare human-annotated multilingual safety resource. It also shows that safety gains in one language need not come at another's expense.

Key points

  • Aya Red-teaming dataset: about 900 human-written harmful prompts for each of eight languages: Arabic, English, Filipino, French, Hindi, Russian, Serbian and Spanish. Each prompt includes an English translation, harm categories and a global/local label. It is hosted as CohereForAI/aya_redteaming on Hugging Face.
  • Safety-only preference data: built from 100 seeds per language (50 global, 50 local). Command R+ rephrased the seeds, Command R+ and Aya 23 (35B) produced responses, and GPT-4 chose the preferred one.
  • General-purpose preference data: 10,000 examples sampled from UltraFeedback Binarized (61,135 in total) and translated with NLLB-3.3B. General quality was measured by win-rates on Multilingual Dolly-200.
  • Main results: preference training (DPO) beat SFT, cutting harmful generations by 54.7% relative while reaching 71% win-rate over the base model. Harm fell by at least 37% in every language.
  • Cross-harm transfer: training only on global harms also reduced local harms, though less strongly. Adding local-harm examples in turn helped with global harms.

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

 Institutions

🎓University of CambridgeCambridge · United Kingdom243 works · 121,396 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksSocial Sciences · 1,772 worksSafety Research · 543 works

🧭 Topics 2Research subjects and languages the work is about.Multilingual & cross-lingual NLP · 828 worksBias, fairness & safety · 517 works

Cite

Cite as Aakanksha, Arash Ahmadian, Beyza Ermis, Seraphina Goldfarb-Tarrant, Julia Kreutzer, Marzieh Fadaee and Sara Hooker. 2024. The Multilingual Alignment Prism: Aligning Global and Local Preferences to Reduce Harm. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2024.emnlp-main.671

@inproceedings{aakanksha2024multilingual,
  title = {{The Multilingual Alignment Prism: Aligning Global and Local Preferences to Reduce Harm}},
  author = {Aakanksha, Arash Ahmadian, Beyza Ermis, Seraphina Goldfarb-Tarrant, Julia Kreutzer, Marzieh Fadaee and Sara Hooker},
  booktitle = {Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing},
  year = {2024},
  doi = {10.18653/v1/2024.emnlp-main.671},
  url = {https://doi.org/10.18653/v1/2024.emnlp-main.671}
}

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