HyP-KGRAG: Hypothetical Path-Based Knowledge Graph Retrieval Augmented Generation with DeepSeek

Large Language Models (LLMs) often produce hallucinated or factually incorrect responses in domain-specific
question answering (QA) tasks. To address this limitation, this work explores the integration of Knowledge
Graphs (KGs) with Retrieval-Augmented Generation (RAG) as a strategy to improve factual accuracy and multi-
hop evidence selection. Specifically, the benefits of using structured information from a KG to enhance the
QA performance of the DeepSeek model are investigated. A novel framework, HyP-KGRAG, is introduced in
which KG triples are retrieved via hypothetical paths and refined through an LLM-based denoising module.
Experimental results on the material science MSE-KG dataset show that HyP-KGRAG significantly improves the
QA performance of DeepSeek and other baseline models, achieving a ROUGE-1 F1 score of 0.532 and an SBERT
similarity of 0.629.