Tokenization, Fusion and Decoupling: Bridging the Granularity Mismatch Between Large Language Models and Knowledge Graphs

Researchers propose KGT, a novel framework that bridges the gap between Large Language Models and Knowledge Graph Completion by using dedicated entity tokens for full-space prediction. The approach addresses fundamental granularity mismatches through specialized tokenization, feature fusion, and decoupled prediction mechanisms.