Retrieval Augmented Generation (RAG)'s success depends on the utility the LLM derives from the content used for grounding. Quantifying content utility does not have a definitive specification and existing metrics ignore model-specific capabilities and/or rely on costly annotations. In this paper, we propose Grounding Generation Utility (GroGU), a model-specific and reference-free metric that defines utility as a function of the downstream LLM's generation confidence based on entropy. Despite having no annotation requirements, GroGU is largely faithful in distinguishing ground-truth documents while capturing nuances ignored by LLM-agnostic metrics. We apply GroGU to train a query-rewriter for RAG by identifying high-utility preference data for Direct Preference Optimization. Experiments show improvements by up to 18.2 points in Mean Reciprocal Rank and up to 9.4 points in answer accuracy.
@article{arxiv.2601.23129,
title = {Evaluating the Utility of Grounding Documents with Reference-Free LLM-based Metrics},
author = {Yilun Hua and Giuseppe Castellucci and Peter Schulam and Heba Elfardy and Kevin Small},
journal= {arXiv preprint arXiv:2601.23129},
year = {2026}
}