LLM Scheming Inversely Scales with Pretraining Language Coverage
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Computer Science > Artificial Intelligence
Title:LLM Scheming Inversely Scales with Pretraining Language Coverage
Abstract:With the growing capabilities of frontier models, AI alignment becomes increasingly critical in high-risk deployment settings. While recent work has empirically demonstrated in-context scheming -- the covert pursuit of misaligned objectives while feigning alignment -- in frontier language models, most work has been performed exclusively in English, leaving a major gap in multilingual safety. We apply Petri, an open-source automated auditing framework, to Qwen3-30B-A3B to evaluate deceptive and scheming behaviors across multiple languages. Our findings suggest that scheming scores are inversely correlated with the estimated pretraining language coverage, with low-resource languages averaging 34.2\% higher scores compared to high-resource languages on a five-category scheming index. Furthermore, we find that the effect of estimated pretraining language coverage is not uniform across scheming behaviors.
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2607.24769 [cs.AI] |
| (or arXiv:2607.24769v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.24769
arXiv-issued DOI via DataCite
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