Soteria: Language-Specific Functional Parameter Steering for Multilingual Safety Alignment

• URL: http://arxiv.org/abs/2502.11244v1
• Date: Sun, 16 Feb 2025 19:44:01 GMT
• Title: Soteria: Language-Specific Functional Parameter Steering for Multilingual Safety Alignment
• Authors: Somnath Banerjee, Sayan Layek, Pratyush Chatterjee, Animesh Mukherjee, Rima Hazra,
• Abstract summary: Soteria locates and minimally adjusts the "functional heads" most responsible for harmful content generation in each language. XThreatBench is a specialized multilingual dataset capturing fine-grained harmful behaviors drawn from real policy guidelines. Experiments with leading open-source LLMs show that Soteria consistently improves safety metrics across high-, mid-, and low-resource languages.
• Score: 4.368725325557961
• License:
• Abstract: Ensuring consistent safety across multiple languages remains a significant challenge for large language models (LLMs). We introduce Soteria, a lightweight yet powerful strategy that locates and minimally adjusts the "functional heads" most responsible for harmful content generation in each language. By altering only a fraction of parameters, Soteria drastically reduces policy violations without sacrificing overall model performance, even in low-resource settings. To rigorously evaluate our approach, we also present XThreatBench, a specialized multilingual dataset capturing fine-grained harmful behaviors drawn from real policy guidelines. Experiments with leading open-source LLMs (e.g., Llama, Qwen, Mistral) show that Soteria consistently improves safety metrics across high-, mid-, and low-resource languages. These findings highlight a promising path toward scalable, linguistically attuned, and ethically aligned LLMs worldwide.

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