RARE: Redundancy-Aware Retrieval Evaluation Framework for High-Similarity Corpora

2026-04-21 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Information Retrieval · Depth: Expert, quick

Summary

The RARE (Redundancy-Aware Retrieval Evaluation) framework addresses a critical mismatch in RAG system evaluation, where existing QA benchmarks assume distinct documents, but real-world corpora like financial reports, legal codes, and patents exhibit high redundancy and inter-document similarity. This discrepancy leads to unfair undervaluation of retrievers and poor generalization from standard benchmarks to practical applications. RARE constructs realistic benchmarks by decomposing documents into atomic facts for precise redundancy tracking and enhancing LLM-based data generation with CRRF, which scores quality criteria separately and fuses decisions by rank. Applying RARE to Finance, Legal, and Patent corpora, the RedQA benchmark reveals significant robustness gaps; a strong retriever baseline drops from 66.4% PerfRecall@10 on 4-hop General-Wiki to 5.0-27.9% PerfRecall@10 at 4-hop depth.

Key takeaway

For AI Architects and AI Engineers deploying RAG systems in domains with highly redundant documents, such as legal or financial, your current benchmark results may not reflect real-world performance. You should adopt the RARE framework to build domain-specific evaluations that account for document redundancy, ensuring your retriever's robustness and preventing significant performance drops in production environments. This will lead to more reliable RAG system deployments.

Key insights

Existing RAG benchmarks fail in redundant, real-world corpora, necessitating a new evaluation framework.

Principles

• Redundancy undermines standard RAG evaluation.
• Atomic fact decomposition tracks information precisely.

Method

RARE constructs benchmarks by decomposing documents into atomic facts for redundancy tracking and uses CRRF to enhance LLM-based data generation by scoring criteria separately and fusing decisions by rank.

In practice

• Apply RARE to build domain-specific RAG evaluations.
• Use CRRF for reliable LLM-based data generation.

Topics

• RARE Framework
• Retrieval-Augmented Generation
• High-Similarity Corpora
• Redundancy-Aware Evaluation
• Atomic Fact Decomposition

Best for: Research Scientist, AI Architect, AI Engineer, AI Scientist, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.