To evaluate knowledge in large language models (LLMs), current methods query the model and then evaluate its generated responses. In this work, we ask whether evaluation can be done $\textit{before}$ the model has generated any text. Concretely, is it possible to estimate how knowledgeable a model is about a certain entity, only from its internal computation? We study this question with two tasks: given a subject entity, the goal is to predict (a) the ability of the model to answer common questions about the entity, and (b) the factuality of responses generated by the model about the entity. Experiments with a variety of LLMs show that KEEN, a simple probe trained over internal subject representations, succeeds at both tasks - strongly correlating with both the QA accuracy of the model per-subject and FActScore, a recent factuality metric in open-ended generation. Moreover, KEEN naturally aligns with the model's hedging behavior and faithfully reflects changes in the model's knowledge after fine-tuning. Lastly, we show a more interpretable yet equally performant variant of KEEN, which highlights a small set of tokens that correlates with the model's lack of knowledge. Being simple and lightweight, KEEN can be leveraged to identify gaps and clusters of entity knowledge in LLMs, and guide decisions such as augmenting queries with retrieval.

评估大型语言模型（LLMs）中的知识能力是否可以在模型生成任何文本之前根据其内部计算来预测，研究表明用内部主题表示训练的简单探测器KEEN在两个任务中都取得成功，能够强烈相关地预测模型对于某个实体回答常见问题的能力和生成的回答关于该实体的事实性，另外，KEEN与模型的回避行为自然地相符，并可以忠实地反映模型微调后的知识变化，最后，我们展示了一个更易解释但同样有效的KEEN变体，能够突出显示与模型知识缺失相关的一小组标记，KEEN简单轻便，可用于识别LLMs中实体知识的差距和聚类，并指导决策，例如通过检索来增强查询。

大型语言模型中的知识估计不需要生成单个标记