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

2026-04-22 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

The RARE (Redundancy-Aware Retrieval Evaluation) framework addresses limitations in existing QA benchmarks for Retrieval-Augmented Generation (RAG) systems, which often fail to account for high information redundancy and inter-document similarity prevalent in real-world corpora like financial reports, legal codes, and patents. RARE constructs realistic benchmarks by decomposing documents into atomic facts for precise redundancy tracking and enhancing LLM-based data generation with CRRF (Criterion-wise Prompting with Reciprocal Rank Fusion). CRRF improves data reliability by scoring criteria separately and fusing decisions by rank. Applying RARE to Finance, Legal, and Patent corpora, the RedQA benchmark reveals significant robustness gaps; a strong retriever baseline dropped from 66.4% PerfRecall@10 on General-Wiki to 5.0–27.9% PerfRecall@10 at 4-hop depth in these high-overlap domains. The framework enables practitioners to build domain-specific RAG evaluations that accurately reflect deployment conditions.

Key takeaway

For AI Architects and AI Engineers designing RAG systems for specialized domains like Finance or Legal, you should re-evaluate your retriever's robustness using benchmarks that account for high document redundancy and similarity. Your current benchmarks likely overestimate real-world performance. Consider integrating RARE's principles to create more realistic evaluation datasets, focusing on how your retrievers handle near-duplicate information and multi-hop queries in dense, specialized corpora to identify and mitigate critical performance gaps.

Key insights

Existing RAG benchmarks misrepresent real-world performance due to high document redundancy and similarity in enterprise corpora.

Principles

• Decompose documents into atomic facts for precise redundancy tracking.
• Evaluate LLM-generated data quality using criterion-wise prompting and rank fusion.
• Higher document similarity correlates with greater retrieval performance degradation.

Method

RARE constructs RAG benchmarks by selecting valid information, systematically tracking redundancy via embedding similarity and LLM verification, and generating multi-hop questions. It uses CRRF for stable multi-criteria ranking of atomic units and questions.

In practice

• Use RARE to build domain-specific RAG evaluations for high-overlap corpora.
• Implement CRRF for more reliable LLM-based data generation and quality control.
• Prioritize diversity in retrieval for multi-hop queries in redundant corpora.

Topics

• Redundancy-Aware Retrieval Evaluation
• Retrieval-Augmented Generation
• LLM-based Data Generation
• CRRF
• High-Similarity Corpora

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 cs.CL updates on arXiv.org.