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Knowledge Distillation for Temporal Knowledge Graph Reasoning with Large Language Models
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This method proposes a two-stage knowledge distillation framework where LLMs act as teacher models to transfer structural and temporal reasoning capabilities to lightweight student models. The goal is to improve the performance and efficiency of temporal knowledge graph reasoning (a KG completion task) by leveraging the advanced reasoning signals from LLMs.
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