A Fixed-Budget, Cluster-Aware Standard for LLM-as-a-Judge Evaluation: A Multi-Hop RAG Stress Test
arXiv:2605.27789v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) systems are often compared by asking a large language model (LLM) judge which answer is better. For multi-hop RAG, this has become a measurement problem as much as a modeling problem: the same score can reflect retrieval quality, answer length, lexical overlap, or a statistical test that ignores clustered data. We ask what happens when these choices are made explicit. We propose a minimum measurement standard for LLM-as-a-judge comparisons in RAG. The standard fixes the top-100 candidate pool, evidence budget,
As LLM-as-a-Judge becomes a prevalent method for evaluating RAG systems, the need for standardized and robust measurement protocols is becoming critical to ensure reliable comparisons and progress.
This standardization directly addresses the 'measurement problem' in LLM evaluation, providing a more reliable foundation for comparing the effectiveness of different RAG implementations and accelerating advancements in AI capabilities.
The proposed standard fixes key variables like candidate pool and evidence budget, moving evaluation beyond arbitrary scoring towards a more scientific, comparable, and cluster-aware methodology.
- · AI researchers
- · RAG system developers
- · Enterprises deploying RAG
- · Unstandardized LLM-as-a-Judge methodologies
- · Systems with inflated performance metrics due to flawed evaluation
Improved accuracy and comparability of RAG system evaluations, leading to clearer progress in the field.
Faster development and deployment of more effective multi-hop RAG systems across various applications.
Increased trust and adoption of RAG technologies in complex, critical applications where reliability is paramount.
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Read at arXiv cs.AI