Who Bridges Safety? Identifying and Targeting Cross-Lingual Shared Safety Pathways
2026-08-10 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors studied how large language models avoid harmful content across different languages. Instead of looking at single parts, they examined how safety signals move through the model's layers, finding shared pathways that help transfer safety features from languages with lots of data to those with less. They then improved safety in low-resource languages by fine-tuning just these shared pathways, without affecting the model's overall performance. This work helps explain and enhance multilingual safety in AI models.
large language modelssafety capabilitiesmechanistic interpretabilitycross-lingualhigh-resource languageslow-resource languagessignal propagationmodel alignmentneural pathwaysharmful content detection
Authors
Shuyi Miao, Wangjie Qiu, Pengyang Shao, Canran Xiao, Fei Shen, Zhiming Zheng, Tat-Seng Chua
Abstract
Uncovering the internal mechanisms underlying the safety capabilities of large language models (LLMs) is crucial for developing trustworthy artificial intelligence. Currently, mechanistic interpretability studies on multilingual safety are largely confined to local components, such as isolated neurons. However, this static and fragmented perspective overlooks the synergy among components and fails to elucidate how safety signals dynamically propagate within the model to drive safety decisions ultimately. In this work, we move beyond isolated neurons to identify and target the cross-layer functional pathways formed during safety signal propagation, thereby uncovering the mechanisms driving the cross-lingual safety gap. Specifically, we first identify monolingual safety pathways and validate their impact on refusing harmful requests. Subsequent cross-lingual analyses reveal a sparse subset of cross-lingual shared safety pathways, confirming that this intersection acts as the internal bridge transferring safety capabilities from high-resource (HR) languages to non-high-resource (NHR) languages. Building on these mechanistic findings, we propose a pathways-targeted alignment method based on the cross-lingual shared safety pathways. Experimental results show that updating only a small fraction of pathway parameters significantly improves safety in NHR languages while largely preserving the model's general capabilities.