arXiv:2604.19047v2 Announce Type: replace Abstract: Existing QA benchmarks typically assume distinct documents with minimal overlap, yet real-world retrieval-augmented generation (RAG) systems operate on corpora such as financial reports, legal codes, and patents, where information is highly redundant and documents exhibit strong inter-document similarity. This mismatch undermines evaluation validity: retrievers can be unfairly undervalued even when they retrieve documents that provide sufficient evidence, because redundancy across documents is not accounted for in evaluation. On the other han
The proliferation of Retrieval-Augmented Generation (RAG) systems in enterprise settings necessitates more robust and realistic evaluation methodologies that account for the messy, redundant nature of real-world data.
This framework directly addresses a significant limitation in current AI evaluation, providing a more accurate measure of RAG system effectiveness, especially in critical domains like finance and legal where data overlap is common.
The development of RARE changes how RAG systems will be benchmarked, pushing researchers and developers to build more resilient and context-aware retrieval mechanisms rather than relying on unrealistic clean corpus assumptions.
- · AI researchers focused on retrieval-augmented generation
- · Enterprises deploying RAG in data-rich domains (e.g., legal, finance)
- · Companies developing RAG evaluation tools
- · Developers of RAG systems capable of handling redundancy
- · Legacy RAG evaluation metrics that ignore redundancy
- · AI models that perform poorly in redundant data environments
- · Benchmarking organizations using oversimplified datasets
RARE provides a new standard for evaluating Retrieval-Augmented Generation (RAG) systems, particularly in high-similarity corpora.
Improved RAG evaluation will lead to the development of more robust RAG models that can effectively synthesize information from redundant sources.
More effective RAG systems could accelerate the adoption of AI agents in complex, data-heavy white-collar workflows, improving efficiency and decision-making.
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Read at arXiv cs.CL