RFKG-CoT: Relation-Driven Adaptive Hop-count Selection and Few-Shot Path Guidance for Knowledge-Aware QA

Chao Zhang; Minghan Li; Tianrui Lv; Guodong Zhou

AAAI 2026technical0 citations

RFKG-CoT: Relation-Driven Adaptive Hop-count Selection and Few-Shot Path Guidance for Knowledge-Aware QA

Chao Zhang, Minghan Li, Tianrui Lv, Guodong Zhou

Abstract

Large language models (LLMs) often generate hallucinations in knowledge-intensive QA due to parametric knowledge limitations. While existing methods like KG-CoT improve reliability by integrating knowledge graph (KG) paths, they suffer from rigid hop-count selection (solely question-driven) and underutilization of reasoning paths (lack of guidance). To address this, we propose RFKG-CoT: First, it replaces the rigid hop-count selector with a relation-driven adaptive hop-count selector that dynamically adjusts reasoning steps by activating KG relations (e.g., 1-hop for direct

BibTeX

@inproceedings{aaai2026_rfkgcotrelationd,
  title = {RFKG-CoT: Relation-Driven Adaptive Hop-count Selection and Few-Shot Path Guidance for Knowledge-Aware QA},
  author = {Chao Zhang and Minghan Li and Tianrui Lv and Guodong Zhou},
  booktitle = {AAAI 2026},
  year = {2026}
}