How Fine-Grained Should a RAG Benchmark Be? A Hierarchical Framework for Synthetic Question Generation
arXiv:2606.12789v1 Announce Type: new Abstract: Evaluating retrieval-augmented generation (RAG) systems requires benchmarks that capture diverse question characteristics, yet practitioners lack empirical guidance on which dimensions to vary and at what granularity. We present HieraRAG, a hierarchical framework for studying granularity in RAG benchmark construction, defining optimal granularity as the level that maximizes discriminative power (the standard deviation of generation quality across categories) within a given RAG configuration. As a case study, we generate 5,872 synthetic question-a
As Retrieval-Augmented Generation (RAG) systems become central to AI applications, the need for robust and diverse benchmarks is growing to ensure reliable and effective deployments.
Improved RAG benchmarking directly impacts the performance, trustworthiness, and widespread adoption of AI agents and enterprise AI solutions, influencing market leaders and laggards.
The proposed hierarchical framework offers a more systematic way to evaluate RAG systems, potentially leading to more targeted research and development efforts in AI.
- · AI developers
- · RAG system providers
- · Enterprise AI adopters
- · Developers with poor RAG benchmarks
- · Generic AI evaluation methodologies
Better evaluation metrics accelerate the development of more capable and reliable RAG systems.
Enhanced RAG performance could lead to a faster integration of AI agents into complex business workflows.
More robust AI systems, validated by superior benchmarks, may accelerate the broader impact of AI on productivity and economic structures.
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Read at arXiv cs.CL