Papers with RAG robustness

2 papers
An Automatic Method to Estimate Correctness of RAG (2025.coling-industry)

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Challenge: Existing methods to assess the correctness of RAG models fail to capture the model’s internal state during answer generation.
Approach: They propose a method to predict the correctness of RAG models by modeling the model’s uncertainty on quantified perturbations of input.
Outcome: Extensive experiments across multiple large language models show that the proposed approach quantifies RAG robustness by aligning predictions with ground truth with a MSE 0.002 while offering flexibility for diverse qualitative metrics.
Typos that Broke the RAG’s Back: Genetic Attack on RAG Pipeline by Simulating Documents in the Wild via Low-level Perturbations (2024.findings-emnlp)

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Challenge: Existing studies on the robustness of Large Language Models (LLMs) overlook the interconnected relationships between RAG components or the potential threats prevalent in real-world databases, such as minor textual errors.
Approach: They propose a novel attack method that exploits vulnerabilities in RAG components and tests its robustness against noisy documents.
Outcome: The proposed method devastates the performance of each component and their synergy, and significantly devases the performance.

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