Although large-scale pre-trained language models (PTLMs) are shown to encode rich knowledge in their model parameters, the inherent knowledge in PTLMs can be opaque or static, making external knowledge necessary. However, the existing information retrieval techniques could be costly and may even introduce noisy and sometimes misleading knowledge. To address these challenges, we propose the instance-level adaptive propulsion of external knowledge (IAPEK), where we only conduct the retrieval when necessary. To achieve this goal, we propose measuring whether a PTLM contains enough knowledge to solve an instance with a novel metric, Thrust, which leverages the representation distribution of a small number of seen instances. Extensive experiments demonstrate that thrust is a good measurement of PTLM models' instance-level knowledgeability. Moreover, we can achieve significantly higher cost-efficiency with the Thrust score as the retrieval indicator than the naive usage of external knowledge on 88% of the evaluated tasks with 26% average performance improvement. Such findings shed light on the real-world practice of knowledge-enhanced LMs with a limited knowledge-seeking budget due to computation latency or costs.

翻译：尽管大规模预训练语言模型（PTLMs）被证明在其模型参数中编码了丰富的知识，但PTLMs中的固有知识可能不透明或静态，这使得外部知识成为必要。然而，现有的信息检索技术可能成本高昂，甚至引入噪音甚至误导性知识。为应对这些挑战，我们提出了实例级别的自适应外部知识推进（IAPEK），其中仅在必要时进行检索。为实现这一目标，我们提出了一种新颖的度量方法Thrust，通过利用少量已见实例的表示分布，来衡量PTLM是否包含足够的知识来解决某个实例。大量实验表明，Thrust是衡量PTLM模型实例级别知识性的良好指标。此外，相比简单使用外部知识，在88%的评估任务上，以Thrust分数作为检索指标可实现显著更高的成本效率，平均性能提升26%。这些发现为在计算延迟或成本导致知识寻求预算有限的实际场景中应用知识增强型语言模型提供了启示。