Semantic Stratification Improves Retrieval Evaluation for RAG
A new arXiv paper (2604.20763) formalizes retrieval evaluation as a statistical estimation problem, revealing that current heuristic query sets introduce hidden bias. The authors propose semantic stratification, which organizes documents into entity-based clusters and generates queries for missing strata. This method provides formal coverage guarantees and interpretable failure mode analysis. Experiments across multiple benchmarks show systematic coverage gaps and structural signals explaining variance in retrieval performance.
Key facts
- arXiv:2604.20763v1
- Retrieval quality is the primary bottleneck for accuracy and robustness in RAG
- Current evaluation uses heuristically constructed query sets with hidden intrinsic bias
- Semantic stratification grounds evaluation in corpus structure via entity-based clusters
- Method yields formal semantic coverage guarantees and interpretable visibility into failure modes
- Experiments conducted across multiple benchmarks and retrieval methods
- Results expose systematic coverage gaps and structural signals explaining variance
Entities
Institutions
- arXiv