Certified Domain Consistency for Multi-Domain Retrieval: Label-Free Per-Domain Contamination Control with Conformal Risk Guarantees
Researchers introduce C3R, a method to prevent incorrect domain evidence from polluting multi-domain retrieval tasks. It uses conformal risk control to ensure reliability without needing labels at query time.
- C3R prevents silent failures in multi-domain retrieval by controlling domain-specific contamination.
- The method works as a drop-in layer and does not require query-time labels.
- It uses conformal risk control to provide mathematical guarantees on error rates.
- The system can choose to abstain from answering if the risk of domain error is too high.
Retrieval systems often struggle when searching through mixed datasets containing multiple distinct domains. Standard ranking metrics frequently fail to detect when a system retrieves relevant information that actually belongs to the wrong domain, leading to silent errors in downstream tasks.
The proposed C3R framework acts as a drop-in control layer that manages these errors. By utilizing an inferred domain posterior, the system can certify a specific contamination budget for each domain. If the risk of error exceeds a certain threshold, the system chooses to abstain rather than providing potentially misleading results.
This approach is particularly significant because it does not require manual labels at the time of the query. It relies on a two-split scheme to provide mathematical guarantees, ensuring that even in the most difficult domains, the error rate is controlled rather than ignored.
Provides a way to implement safety layers in RAG pipelines to prevent domain mismatch.
Offers a new way to apply conformal prediction to retrieval-augmented generation.
Improves the reliability of AI search engines by preventing irrelevant domain data from being used.
- Conformal Risk Control
- A framework for providing statistical guarantees on the error rates of machine learning models.
- Domain Contamination
- When a retrieval system pulls information from a domain that is irrelevant to the user's specific context.
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