![[2602.20926] HELP: HyperNode Expansion and Logical Path-Guided Evidence Localization for Accurate and Efficient GraphRAG](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.20926] HELP: HyperNode Expansion and Logical Path-Guided Evidence Localization for Accurate and Efficient GraphRAG
Summary
This article presents the HELP framework, which enhances Retrieval-Augmented Generation (RAG) by addressing knowledge boundaries and hallucinations in Large Language Models (LLMs) through HyperNode Expansion and Logical Path-Guided Evidence Localization.
Why It Matters
As LLMs become increasingly integral to knowledge-intensive tasks, improving their accuracy and efficiency is crucial. The HELP framework offers a solution to common limitations in existing RAG approaches, potentially transforming how AI systems retrieve and utilize information, thus enhancing their reliability in practical applications.
Key Takeaways
- HELP improves accuracy and efficiency in GraphRAG applications.
- HyperNode Expansion captures complex dependencies for better reasoning.
- Logical Path-Guided Evidence Localization enhances retrieval speed.
- The framework shows significant performance improvements in QA benchmarks.
- HELP reduces retrieval latency while maintaining knowledge integrity.
Computer Science > Artificial Intelligence arXiv:2602.20926 (cs) [Submitted on 24 Feb 2026] Title:HELP: HyperNode Expansion and Logical Path-Guided Evidence Localization for Accurate and Efficient GraphRAG Authors:Yuqi Huang, Ning Liao, Kai Yang, Anning Hu, Shengchao Hu, Xiaoxing Wang, Junchi Yan View a PDF of the paper titled HELP: HyperNode Expansion and Logical Path-Guided Evidence Localization for Accurate and Efficient GraphRAG, by Yuqi Huang and 6 other authors View PDF HTML (experimental) Abstract:Large Language Models (LLMs) often struggle with inherent knowledge boundaries and hallucinations, limiting their reliability in knowledge-intensive tasks. While Retrieval-Augmented Generation (RAG) mitigates these issues, it frequently overlooks structural interdependencies essential for multi-hop reasoning. Graph-based RAG approaches attempt to bridge this gap, yet they typically face trade-offs between accuracy and efficiency due to challenges such as costly graph traversals and semantic noise in LLM-generated summaries. In this paper, we propose HyperNode Expansion and Logical Path-Guided Evidence Localization strategies for GraphRAG (HELP), a novel framework designed to balance accuracy with practical efficiency through two core strategies: 1) HyperNode Expansion, which iteratively chains knowledge triplets into coherent reasoning paths abstracted as HyperNodes to capture complex structural dependencies and ensure retrieval accuracy; and 2) Logical Path-Guided Evidenc...
