Title: Align Once, Benefit Multilingually: Enforcing Multilingual Consistency for LLM Safety Alignment
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: Multilingual Enhancement, Large Language Models

Abstract: The widespread deployment of large language models (LLMs) across linguistic communities necessitates reliable multilingual safety alignment. However, recent efforts to extend alignment to other languages often require substantial resources, either through large-scale, high-quality supervision in the target language or through pairwise alignment with high-resource languages, which limits scalability.
In this work, we propose a resource-efficient method for improving multilingual safety alignment. 
We introduce a plug-and-play Multi-Lingual Consistency (MLC) loss that can be integrated into existing monolingual alignment pipelines. 
By improving collinearity between multilingual representation vectors, our method encourages directional consistency at the multilingual semantic level in a single update. This allows simultaneous alignment across multiple languages using only multilingual prompt variants without requiring additional response-level supervision in low-resource languages. We validate the proposed method across different model architectures and alignment paradigms, and demonstrate its effectiveness in enhancing multilingual safety with limited impact on general model utility. Further evaluation across languages and tasks indicates improved cross-lingual generalization, suggesting the proposed approach as a practical solution for multilingual consistency alignment under limited supervision.

TL;DR: We propose a multilingual consistency loss that can be plugged into existing alignment pipelines to improve multilingual safety of LLMs efficiently.