Year: 2026
Type: conference-paper
Abstract: Despite the rapid advancements of Large Language Models (LLMs), safety risks remain a critical challenge for low-resource languages.Existing safety datasets are predominantly English-centric, limiting progress in multilingual safety alignment.As a result, low-resource expert models-fine-tuned on their respective instruction datasets-tend to exhibit higher unsafety rates compared to their high-resource counterparts.In this work, we propose a safetyaware layer swapping method that transfers safety alignment from an English safety expert to low-resource language experts without additional training.To further enhance transferability, our method adaptively selects or blends modules based on their degree of specialization.Our approach preserves performance on general language understanding tasks while enhancing safety in the target languages.Experimental results show that the proposed method achieves comparable performance to the language expert on general benchmarks such as MMMLU, BELEBELE, and MGSM, while producing more aligned and less harmful responses on the MultiJail safety benchmark.1
Authors: Hyunseo Shin, Wonseok Hwang
Institution: University of Seoul
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7140074690