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This method proposes an end-to-end framework to decode factual knowledge embedded in LLM latent representations into a dynamic knowledge graph, utilizing an extended activation patching technique. This framework enables layer-wise interpretability analyses (both local and global) of LLMs' internal knowledge resolution process, thereby enhancing the understanding of LLM mechanisms through KG-based visualization and analysis.
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