GraphER: An Efficient Graph-Based Enrichment and Reranking Method for Retrieval-Augmented Generation
arXiv:2603.24925v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) systems that rely on semantic search often fail to retrieve the complete set of evidence for complex queries, particularly when information is distributed across multiple sources. Existing approaches either rely on iterative agentic retrieval, which can be inefficient, or maintain additional structures such as knowledge graphs, which introduce storage and maintenance overhead. In this paper, we propose GraphER, a graph-based enrichment and reranking framework that (1) leverages the organizational structure
The increasing complexity of AI systems, particularly RAG models, is driving the need for more efficient and robust methods to retrieve and process information.
Improving RAG efficiency and accuracy addresses a critical bottleneck in deploying advanced AI, making these systems more practical and scalable for complex tasks.
Retrieval-augmented generation systems can now potentially handle more complex queries and distributed information without significant overhead or inefficient iterative processes.
- · AI developers
- · Generative AI companies
- · Data-intensive industries
- · Enterprises adopting RAG
- · Inefficient RAG approaches
- · Companies reliant on simple semantic search
- · Iterative agentic retrieval systems
Increased efficiency and accuracy in RAG systems for complex queries.
Faster development and deployment of more capable AI agents across industries.
Enhanced automation of knowledge work, leading to productivity gains and changes in workforce requirements.
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Read at arXiv cs.LG