Models perform much worse when forced to generate alternative phrasings (by blocking their preferred tokens), and this off-path robustness depends heavily on model size and safety training—a critical gap to test before deployment.
This paper introduces Decoding-Level Taboo, a stress test that forces language models off their typical generation paths by masking high-probability tokens during decoding.