Learning Generative Knowledge Graph Completion via Path Supervision from Link Predictors

We propose Path-Augmented Retrieval and Reasoning (PARR), a framework for training generative language models for Knowledge Graph Completion (KGC). A unique challenge in adopting native LLM for KGC is the lack of retrieval and reasoning supervision from KGC datasets, which results in all previous efforts to underperform traditional KGC methods by a significant margin. In this work, we propose to leverage grounding paths sampled from interpretable link predictors to provide effective supervision for LLM-based KGC. Specifically, we utilize paths (1) to augment retrieval for enhanced sub-graph retrieval, (2) as ground-truth retrieval signals to supervise Rewriter LLM for KG-based query rewritings, and (3) as a means to "distill" the structural knowledge to a Reasoner LLM through chain-of-thoughts. To further improve reasoning capacity and reduce cascaded error, we devise a RL-based finetuning stage with soft verifiable rewards tailored for KGC. PARR is the first native LLM-based framework that surpasses traditional discriminative KGC methods, achieves superior performance compared to existing state-of-the-arts across a wide range of KG datasets under both transductive and inductive settings, while being generalizable, scalable, and interpretable.