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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Sep 12·5 min read
MOSAIC: Query-Aware Exploration Policy Adaptation for GraphRAG
TL;DR
Mosaic is a novel framework for Graph Retrieval-Augmented Generation (GraphRAG) that adapts exploration policies based on specific query requirements. It significantly improves answer correctness and evidence recall, outperforming previous methods by notable margins.
✦ Why It Matters
Implement Mosaic's adaptive exploration policies to enhance your GraphRAG systems today.
Key Takeaways
- Adopt query-specific exploration policies for improved retrieval accuracy.
- Utilize Mosaic to reduce the number of evaluated paths significantly.
- Leverage high Evidence Recall and Context Relevancy in medical applications.
- Apply the policy interface across different datasets without retraining.
How It Works
Mosaic employs a large language model (LLM) to analyze the specific requirements of each query, allowing it to dynamically adjust the exploration strategy. This includes optimizing seed selection, determining how to traverse the graph, when to stop searching, and how to select relevant evidence.
By treating retrieval as a control problem, Mosaic can efficiently navigate the corpus graph to find the most pertinent information for each unique query.