Graph Alignment Topology as an Inductive Bias for Grounding Detection

Large Language Models (LLMs) are optimized for plausible continuations rather than explicitly verifying if generated propositions are entailed by source documents, limiting their use in critical domains. This research proposes leveraging alignment topology as an inductive bias by constructing aligned bipartite graphs between reference information and LLM outputs, then training a Graph Neural Network (GNN).