Source-Aware Reranking for Retrieval-Augmented Generation: A Reliability Prior Approach
Quick Answer
This study introduces a source-aware reranking method for Retrieval-Augmented Generation (RAG) that incorporates source reliability priors, improving Precision@5 from 0.48 to 0.72 on a health-domain corpus.
Quick Take
The approach reweights retrieval scores based on document source credibility, effectively reducing adversarial document retrieval risks. Experiments were conducted on the Rosie high-performance computing cluster.
Key Points
- Source-aware reranking improves Precision@5 from 0.48 to 0.72.
- Retrieval scores are reweighted using domain-informed source reliability priors.
- Method reduces average adversarial document retrieval risks.
- Experiments conducted on the Rosie high-performance computing cluster.
- Focuses on a 120-document health-domain corpus.
Paper Resources
Source Excerpt
Standard pipelines rank retrieved documents by semantic similarity alone, without accounting for source provenance or credibility. This work evaluates a simple and interpretable modification to RAG retrieval ranking that incorporates domain-informed source reliability priors. Each document is assigned a prior lambda(s) based on its source type, and retrieval scores are reweighted using score(q, d) = sim(q, d) * lambda(s). The framework is evaluated against a simila
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