The paper "Knowledge Circuits in Pretrained Transformers" by Yunzhi Yao et al. explores the inner workings of large language models (LLMs) by focusing on the computation graph to uncover the knowledge circuits that are crucial for articulating specific knowledge. The authors use GPT2 and TinyLLAMA to observe how information heads, relation heads, and Multilayer Perceptrons (MLPs) collaboratively encode knowledge within the model. They evaluate the impact of current knowledge editing techniques on these knowledge circuits, providing insights into their functioning and constraints. Additionally, they utilize knowledge circuits to analyze and interpret language model behaviors such as hallucinations and in-context learning. The study reveals that knowledge circuits can effectively represent implicit neural knowledge representations, elucidate internal mechanisms for knowledge editing, and facilitate the interpretation of language model behaviors. The findings suggest that knowledge circuits hold potential for advancing our understanding of Transformers and guiding the design of improved knowledge editing methods.The paper "Knowledge Circuits in Pretrained Transformers" by Yunzhi Yao et al. explores the inner workings of large language models (LLMs) by focusing on the computation graph to uncover the knowledge circuits that are crucial for articulating specific knowledge. The authors use GPT2 and TinyLLAMA to observe how information heads, relation heads, and Multilayer Perceptrons (MLPs) collaboratively encode knowledge within the model. They evaluate the impact of current knowledge editing techniques on these knowledge circuits, providing insights into their functioning and constraints. Additionally, they utilize knowledge circuits to analyze and interpret language model behaviors such as hallucinations and in-context learning. The study reveals that knowledge circuits can effectively represent implicit neural knowledge representations, elucidate internal mechanisms for knowledge editing, and facilitate the interpretation of language model behaviors. The findings suggest that knowledge circuits hold potential for advancing our understanding of Transformers and guiding the design of improved knowledge editing methods.