Decoding-Level Taboo: A Diagnostic Stress Test for LLM Robustness
Large language model evaluations typically focus on performance under nominal conditions, creating an illusion of capability where models comfortably walk a narrow, highly optimized generation corridor. In real-world deployments, however, complex system prompts, safety guardrails, and structural constraints continuously force models off this nominal path, driving a divergence between benchmark scores and deployment performance. To address this issue, we introduce Decoding-Level Taboo, a zero-prompt diagnostic stress test that intervenes directly in logit space at runtime, forcing models out of their nominal paths. By dynamically masking primary candidate tokens at word boundaries, Taboo f...
| Authors | Tadanobu Chuyo Kamijo, Ori Rottenstreich, Javier Conde et al. |
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| Primary category | cs.CL |
| Published | 10 August 2026 |
| Last updated at source | 11 August 2026 |
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