做AI安全或模型评估的团队，这个基准直接戳中了当前模型最致命的弱点——它们连“不存在的东西”都分不清，建议用PhantomBench测测自家模型。
研究者推出 PhantomBench，这是首个专门评估语言模型对“不存在概念”识别能力的基准，包含超过6万个从真实领域衍生的虚构术语和实体。测试了21个不同规模和类型的模型，发现平均幻觉率高达86.7%，即使是前沿模型在面对预设存在的输入时也几乎无法拒绝回答。该基准可作为研究模型在罕见概念上幻觉行为的代理工具，并提供了可扩展的构建流程。这项工作揭示了模型知识边界认知的严重缺陷，对高风险应用场景构成警示。
PhantomBench: Benchmarking the Non-existential Threat of Language Models
Hallucinations, where language models (LMs) generate factually ungrounded responses, pose serious risks, as users tend to blindly rely on them. This is particularly concerning in high-stakes domains, where consequences of such model behavior can lead to significant harms. Despite notable progress in understanding hallucinations, it remains unclear how reliably these models can recognize the limits of their knowledge. We introduce PhantomBench, the first large-scale benchmark of its kind, comprising more than 60K non-existent terms and entities derived from real concepts across diverse domains. Using our benchmark, we evaluate a total of 21 models of various types and sizes. We show staggering hallucination rates across the board (with average rates as high as 86.7% in some cases), and note that even frontier models surprisingly fail to abstain on non-existent concepts, especially when the input presumes their existence. We then show that PhantomBench can serve as a proxy for studying model behavior on rare concepts for which models are more prone to hallucinate. We also provide a pipeline to construct PhantomBench, enabling scalable generation of non-existent concepts tailored to the specific needs of researchers and practitioners.