We focus on the problem of capturing declarative knowledge in the learned parameters of a language model. We introduce a new model, Entities as Experts (EaE), that can access distinct memories of the entities mentioned in a piece of text. Unlike previous efforts to integrate entity knowledge into sequence models, EaE's entity representations are learned directly from text. These representations capture sufficient knowledge to answer TriviaQA questions such as "Which Dr. Who villain has been played by Roger Delgado, Anthony Ainley, Eric Roberts?". EaE outperforms a Transformer model with $30\times$ the parameters on this task. According to the Lama knowledge probes, EaE also contains more factual knowledge than a similar sized Bert. We show that associating parameters with specific entities means that EaE only needs to access a fraction of its parameters at inference time, and we show that the correct identification, and representation, of entities is essential to EaE's performance. We also argue that the discrete and independent entity representations in EaE make it more modular and interpretable than the Transformer architecture on which it is based.

使用实体作为专家的具有记忆分离能力的新模型（EAE），能够捕获文本中实体的声明性知识，比具有10倍参数的编码-生成变压器模型的性能更好，并比类似大小的BERT和以前整合外部实体知识的方法具有更多的事实知识。

基于实体监督的稀疏记忆访问