Democratizing GraphRAG: Linear, CPU-Only Graph Retrieval for Multi-Hop QA
arXiv:2602.23372v1 Announce Type: cross Abstract: GraphRAG systems improve multi-hop retrieval by modeling structure, but many approaches rely on expensive LLM-based graph construction and GPU-heavy inference. We present SPRIG (Seeded Propagation for Retrieval In Graphs), a CPU-only, linear-time, token-free GraphRAG pipeline that replaces LLM graph building with lightweight NER-driven co-occurrence graphs and uses Personalized PageRank (PPR) for 28% with negligible Recall@10 changes. The results characterize when CPU-friendly graph retrieval helps multi-hop recall and when strong lexical hybrids (RRF) are sufficient, outlining a realistic path to democratizing GraphRAG without token costs or GPU requirements.
arXiv:2602.23372v1 Announce Type: cross Abstract: GraphRAG systems improve multi-hop retrieval by modeling structure, but many approaches rely on expensive LLM-based graph construction and GPU-heavy inference. We present SPRIG (Seeded Propagation for Retrieval In Graphs), a CPU-only, linear-time, token-free GraphRAG pipeline that replaces LLM graph building with lightweight NER-driven co-occurrence graphs and uses Personalized PageRank (PPR) for 28% with negligible Recall@10 changes. The results characterize when CPU-friendly graph retrieval helps multi-hop recall and when strong lexical hybrids (RRF) are sufficient, outlining a realistic path to democratizing GraphRAG without token costs or GPU requirements.
Executive Summary
The article introduces SPRIG, a novel GraphRAG pipeline that enables CPU-only, linear-time graph retrieval for multi-hop QA. By replacing LLM-based graph construction with NER-driven co-occurrence graphs and utilizing Personalized PageRank, SPRIG achieves significant performance gains without relying on expensive GPU resources or token costs. This approach has the potential to democratize GraphRAG, making it more accessible and efficient for various applications.
Key Points
- ▸ Introduction of SPRIG, a CPU-only GraphRAG pipeline
- ▸ Replacement of LLM-based graph construction with NER-driven co-occurrence graphs
- ▸ Utilization of Personalized PageRank for improved performance
Merits
Efficiency
SPRIG's CPU-only and linear-time approach reduces computational costs and increases efficiency
Accessibility
Democratization of GraphRAG through SPRIG makes it more accessible to a wider range of applications and users
Demerits
Limited Contextual Understanding
SPRIG's reliance on NER-driven co-occurrence graphs may limit its ability to capture complex contextual relationships
Expert Commentary
The introduction of SPRIG marks a significant step towards democratizing GraphRAG, enabling more efficient and accessible graph retrieval for multi-hop QA. While SPRIG's approach has its limitations, its potential to reduce computational costs and increase efficiency makes it an attractive solution for various applications. Further research is needed to fully explore the capabilities and limitations of SPRIG and its potential impact on the development of more inclusive and accessible AI systems.
Recommendations
- ✓ Further evaluation of SPRIG's performance in various applications and domains
- ✓ Exploration of potential extensions and improvements to SPRIG's approach, such as incorporating additional contextual information