把大模型逼出舒适区：一个词都不让用，它还能说人话吗
大模型评测都在它最舒服的路径上考它，就像只考你背过的题。这篇论文直接动手术：在生成时把最可能说的那个词屏蔽掉，逼模型绕路。结果发现，模型越大、越经过指令对齐，绕路能力越强——但即便最强的模型，被逼着换词时也会开始胡言乱语。这提醒我们，日常用到的模型，一旦遇到系统提示词、安全限制这些“绕路”场景，表现可能远不如基准分那么光鲜。它不是你明天能用上的工具，但解释了为什么AI在复杂任务里偶尔会突然犯傻。
📄 原文摘要(英文)
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 forces machine circumlocution. Evaluating Taboo across several open-weight model families reveals that off-path robustness is heavily influenced by both parameter scale and post-training instruction alignment, with robustness generally improving with model size and alignment. Beyond the results presented in this paper, Taboo provides a novel primitive for generating diverse synthetic datasets, stress-testing runtime safety guardrails, and auditing model reliability prior to real-world deployment.