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

Source: arXiv cs.CL — read the full report at the original publisher.

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