The remarkable capabilities of modern large language models are rooted in their vast repositories of knowledge encoded within their parameters, enabling them to perceive the world and engage in reasoning. The inner workings of how these models store knowledge have long been a subject of intense interest and investigation among researchers. To date, most studies have concentrated on isolated components within these models, such as the Multilayer Perceptrons and attention head. In this paper, we delve into the computation graph of the language model to uncover the knowledge circuits that are instrumental in articulating specific knowledge. The experiments, conducted with GPT2 and TinyLLAMA, has allowed us to observe how certain information heads, relation heads, and Multilayer Perceptrons collaboratively encode knowledge within the model. Moreover, we evaluate the impact of current knowledge editing techniques on these knowledge circuits, providing deeper insights into the functioning and constraints of these editing methodologies. Finally, we utilize knowledge circuits to analyze and interpret language model behaviors such as hallucinations and in-context learning. We believe the knowledge circuit holds potential for advancing our understanding of Transformers and guiding the improved design of knowledge editing. Code and data are available in https://github.com/zjunlp/KnowledgeCircuits.

翻译：现代大规模语言模型的卓越能力源于其参数中编码的庞大知识库，使其能够感知世界并进行推理。这些模型如何存储知识的内在机制长期以来一直是研究者高度关注和深入探索的课题。迄今为止，大多数研究集中于模型中的孤立组件，例如多层感知机和注意力头。本文中，我们深入探究语言模型的计算图，以揭示在表达特定知识中起关键作用的知识电路。通过在GPT2和TinyLLAMA上进行的实验，我们观察到某些信息头、关系头和多层感知机如何在模型中协同编码知识。此外，我们评估了当前知识编辑技术对这些知识电路的影响，从而更深入地理解这些编辑方法的运作机制与局限。最后，我们利用知识电路分析和解释语言模型的行为，例如幻觉和上下文学习。我们相信知识电路在增进对Transformer的理解以及指导知识编辑的改进设计方面具有潜力。代码与数据可在https://github.com/zjunlp/KnowledgeCircuits获取。