Benign fine-tuning of foundation models induces large, heterogeneous, and often contradictory changes in safety metrics across general and domain-specific benchmarks.
Betterbench: Assessing ai benchmarks, uncovering issues, and establishing best practices.Advances in Neural Information Processing Systems, 37:21763–21813
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Safety Drift After Fine-Tuning: Evidence from High-Stakes Domains
Benign fine-tuning of foundation models induces large, heterogeneous, and often contradictory changes in safety metrics across general and domain-specific benchmarks.