Evaluating RAG Reliability under Clean, Misleading, and Mixed Retrieval

2026-06-05

AI Summary

This study investigates the factual reliability of Retrieval-Augmented Generation (RAG) systems when exposed to misleading information in retrieved passages. By constructing controlled test scenarios comprising clean, contaminated, and mixed evidence, and integrating factual question-answering benchmarks with comparative analyses between parametric knowledge and retrieved evidence, the work proposes an evaluation framework that combines parametric coverage and confidence metrics. It systematically uncovers, for the first time, the mechanisms through which misinformation influences large language model generation, quantifies RAG’s vulnerability under conflicting information, and provides a reproducible methodology along with empirical evidence to enhance its robustness.

Abstract

Retrieval-Augmented Generation (RAG) is widely used to improve the factual reliability of large language models (LLMs) by grounding answers in retrieved evidence. In misinformation-rich environments, however, retrieved content may include plausible but incorrect information, raising concerns about the reliability of RAG-based information access systems. In this work, we propose an evaluation protocol to systematically test how the RAG system handles conflicts between parametric knowledge and evidence retrieved from context with varying amounts of misleading information. We target correct answers to factoid questions that the model responds to correctly, even when there is no retrieval, and use this to test the system with clean, poisoned, and mixed evidence. The proposed analytical framework combines parametric override and confidence metrics to assess when and how misleading information affects the generation process of LLMs. This study aims to provide insights into the robustness of RAG systems in information disorder scenarios.

Problem

• RAG reliability
• misinformation
• retrieval-augmented generation
• factuality
• information disorder

Innovation

• Retrieval-Augmented Generation
• misinformation robustness
• parametric override
• confidence metrics
• evaluation protocol