# Contextual Breach: Assessing the Robustness of Transformer-based QA Models Source: [Contextual Breach: Assessing the Robustness of Transformer-based QA Models]() Collection index: [llms.txt]() Data updated: 2025-11-19T05:01:30.460Z Authors: Asir Saadat, Nahian Ibn Asad Published: 2024-09-17T09:00:11.000Z ## Abstract Contextual question-answering models are susceptible to adversarial perturbations to input context, commonly observed in real-world scenarios. These adversarial noises are designed to degrade the performance of the model by distorting the textual input. We introduce a unique dataset that incorporates seven distinct types of adversarial noise into the context, each applied at five different intensity levels on the SQuAD dataset. To quantify the robustness, we utilize robustness metrics providing a standardized measure for assessing model performance across varying noise types and levels. Experiments on transformer-based question-answering models reveal robustness vulnerabilities and important insights into the model's performance in realistic textual input. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()