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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Sep 9·5 min read
Beyond One-Shot Expansion: Contrastive Evidence Exploration for Multi-Hop Retrieval
TL;DR
A novel multi-hop retrieval framework enhances evidence retrieval for question answering by integrating evidence-conditioned exploration and passage-specific contrastive refinement. This approach significantly improves retrieval quality and downstream QA performance across multiple datasets.
✦ Why It Matters
Implement this framework to enhance your multi-hop retrieval systems and improve QA performance immediately.
Key Takeaways
- Adopt evidence-conditioned exploration for better retrieval accuracy.
- Utilize passage-specific contrastive facets to refine candidate relevance.
- Iteratively generate targeted queries to address unresolved information.
- Test the framework on your existing QA datasets for performance improvements.
How It Works
The framework constructs passage-specific contrastive facets during offline indexing, which characterize each passage in relation to semantically similar neighbors. This allows for fine-grained differentiation among closely related candidates.
During inference, it retrieves evidence iteratively, generating probes to target unresolved information needs and refining candidate relevance using the contrastive facets.