AI RESEARCH
Topology-Aware Reasoning over Incomplete Knowledge Graph with Graph-Based Soft Prompting
arXiv CS.AI
•
ArXi:2604.12503v1 Announce Type: cross Large Language Models (LLMs) have shown remarkable capabilities across various tasks but remain prone to hallucinations in knowledge-intensive scenarios. Knowledge Base Question Answering (KBQA) mitigates this by grounding generation in Knowledge Graphs (KGs). However, most multi-hop KBQA methods rely on explicit edge traversal, making them fragile to KG incompleteness. In this paper, we proposed a novel graph-based soft prompting framework that shifts the reasoning paradigm from node-level path traversal to subgraph-level reasoning.