LUMINA: Detecting Hallucinations in RAG System with Context-Knowledge
  Signals
Authors: name: Min-Hsuan Yeh, name: Yixuan Li, name: Tanwi Mallick

Retrieval-Augmented Generation (RAG) aims to mitigate hallucinations in large
language models (LLMs) by grounding responses in retrieved documents. Yet,
RAG-based LLMs still hallucinate even when provided with correct and sufficient
context. A growing line of work suggests that this stems from an imbalance
between how models use external context and their internal knowledge, and
several approaches have attempted to quantify these signals for hallucination
detection. However, existing methods require extensive hyperparameter tuning,
limiting their generalizability. We propose LUMINA, a novel framework that
detects hallucinations in RAG systems through context-knowledge signals:
external context utilization is quantified via distributional distance, while
internal knowledge utilization is measured by tracking how predicted tokens
evolve across transformer layers. We further introduce a framework for
statistically validating these measurements. Experiments on common RAG
hallucination benchmarks and four open-source LLMs show that LUMINA achieves
consistently high AUROC and AUPRC scores, outperforming prior utilization-based
methods by up to +13% AUROC on HalluRAG. Moreover, LUMINA remains robust under
relaxed assumptions about retrieval quality and model matching, offering both
effectiveness and practicality.