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TGL-LLM is a novel framework that integrates temporal graph learning into LLM-based temporal knowledge graph models. It achieves this through hybrid graph tokenization (using Temporal Graph Adapters and Hybrid Prompt Design) and a two-stage training paradigm (Data Pruning and Prompt Tuning). These components are designed to create a unified representation of knowledge from both temporal graphs and LLMs, addressing limitations in temporal pattern modeling and cross-modal alignment.
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