RePCS: Diagnosing Data Memorization in LLM-Powered Retrieval-Augmented Generation
This provides a lightweight, black-box safeguard for verifying RAG system reliability, especially in safety-critical applications, though it is incremental as it builds on existing diagnostic approaches.
The paper tackles the problem of data memorization in retrieval-augmented generation (RAG) systems, where models may bypass retrieved evidence and rely on memorized training data, by introducing RePCS, a diagnostic method that achieves a ROC-AUC of 0.918 on the Prompt-WNQA benchmark, outperforming prior methods by 6.5 percentage points with low latency overhead.
Retrieval-augmented generation (RAG) has become a common strategy for updating large language model (LLM) responses with current, external information. However, models may still rely on memorized training data, bypass the retrieved evidence, and produce contaminated outputs. We introduce Retrieval-Path Contamination Scoring (RePCS), a diagnostic method that detects such behavior without requiring model access or retraining. RePCS compares two inference paths: (i) a parametric path using only the query, and (ii) a retrieval-augmented path using both the query and retrieved context by computing the Kullback-Leibler (KL) divergence between their output distributions. A low divergence suggests that the retrieved context had minimal impact, indicating potential memorization. This procedure is model-agnostic, requires no gradient or internal state access, and adds only a single additional forward pass. We further derive PAC-style guarantees that link the KL threshold to user-defined false positive and false negative rates. On the Prompt-WNQA benchmark, RePCS achieves a ROC-AUC of 0.918. This result outperforms the strongest prior method by 6.5 percentage points while keeping latency overhead below 4.7% on an NVIDIA T4 GPU. RePCS offers a lightweight, black-box safeguard to verify whether a RAG system meaningfully leverages retrieval, making it especially valuable in safety-critical applications.