The Heterogeneous Safety Impacts of Benign Multilingual Fine-Tuning
Fine-tuning a large language model is a ubiquitous method for enhancing its capability on a specific downstream task. However, prior work has shown that this increase in capability comes with a cost: it can increase a model's tendency to respond to unsafe adversarial prompts, even when fine-tuning with non-adversarial data. We present the first comprehensive empirical study of this phenomenon in multilingual settings by fine-tuning Llama-3.2, Qwen3, and Gemma-3 models using benign data translated across nine languages. We find that safety outcomes are highly sensitive to both the choice of fine-tuning language and the evaluation language, with adversarial compliance rates increasing four-...
| Authors | Will Hawkins, Kaivalya Rawal, Jonathan Rystrøm et al. |
|---|---|
| Primary category | cs.CL |
| Published | 27 June 2026 |
| Last updated at source | 27 June 2026 |
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