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arXiv CS.AI·1d ago·5 min read
Co-adaptive Rule Routing and Constraining for Structured Knowledge Retrieval
TL;DR
CoRE, or Co-Adaptive Rule Routing and Constraining Retrieval, is a novel approach for structured knowledge retrieval that learns to assign reasoning rules to queries without predefined pairs. It effectively improves retrieval performance across various domains by co-adapting routing and retrieval processes through reinforcement and contrastive learning.
✦ Why It Matters
Implement CoRE in your knowledge retrieval systems to enhance performance without needing predefined rules.
Key Takeaways
- Explore CoRE for flexible knowledge retrieval without predefined rules.
- Utilize reinforcement learning to improve routing policies in your applications.
- Consider multi-domain pretraining for better rule transfer across datasets.
- Analyze query patterns to refine and discover new reasoning rules.
How It Works
CoRE operates by using a routing agent that selects reasoning rules for each query. The retriever then gathers evidence from a knowledge base based on the selected rule's constraints.
This process is interdependent; as the routing policy improves through reinforcement learning, the retriever's performance also enhances via contrastive learning. This co-adaptation allows the system to learn effectively without needing predefined query-rule pairs.