LoRA-Adversarial Fine-Tuning for XLM-R Robustness in West African Languages on LSR Benchmarks

Abstract

Abstract: Safety alignment in large language models relies predominantly on English-language training data. When harmful intent is expressed in low-resource languages, refusal mechanisms that hold in English frequently fail to activate. We introduce LSR (Linguistic Safety Robustness), the first systematic benchmark for measuring cross-lingual refusal degradation in West African languages: Yoruba, Hausa, Igbo, and Igala. LSR uses a dual-probe evaluation protocol - submitting matched English and target-language probes to the same model - and introduces Refusal Centroid Drift (RCD), a metric that quantifie

Research Question

How does the integration of LoRA fine-tuning with adversarial training compare to standard fine-tuning in improving XLM-R robustness against adversarial attacks in West African languages, as measured by LSR benchmark scores?

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