arXiv:2606.23695v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) grounds Large Language Models in external knowledge, yet current evaluations rely on discrete heuristics that suffer from ''epistemic blindness'' - failing to distinguish genuine contextual information extraction from parametric memory recall. To address this, we introduce the Normalized Context Utilization (NCU) metric, leveraging continuous token log-probabilities across zero-shot, oracle, and adversarial conditions to strictly quantify contextual information gain. Evaluating architectures ranging from 1.5
The rapid deployment of RAG systems highlights the urgent need for more robust evaluation metrics beyond current heuristic approaches, which suffer from 'epistemic blindness.'
This new metric provides a rigorous way to quantify the actual contextual information gain in RAG, moving beyond superficial evaluations to deeper understanding of system performance.
Evaluation of RAG systems will likely shift from discrete, heuristic-based measures to continuous, log-probability-based metrics, leading to more accurate comparisons and development.
- · AI researchers and developers
- · Organizations deploying RAG systems
- · Robust AI evaluation platforms
- · Heuristic-based RAG evaluation methods
- · Systems relying on 'epistemic blindness'
Improved understanding and benchmarking of RAG systems' ability to leverage external knowledge.
Accelerated development of more effective RAG architectures that genuinely utilize context over parametric memory.
Enhanced trust and reliability in LLM applications grounded in external knowledge, reducing hallucinations and improving factual accuracy.
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Read at arXiv cs.AI