# Are Multilingual BERT models robust? A Case Study on Adversarial Attacks for Multilingual Question Answering Source: [Are Multilingual BERT models robust? A Case Study on Adversarial Attacks for Multilingual Question Answering]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:09:38.543Z Authors: Sara Rosenthal, Mihaela Bornea, Avirup Sil Published: 2021-04-15T17:55:09.000Z ## Abstract Recent approaches have exploited weaknesses in monolingual question answering (QA) models by adding adversarial statements to the passage. These attacks caused a reduction in state-of-the-art performance by almost 50%. In this paper, we are the first to explore and successfully attack a multilingual QA (MLQA) system pre-trained on multilingual BERT using several attack strategies for the adversarial statement reducing performance by as much as 85%. We show that the model gives priority to English and the language of the question regardless of the other languages in the QA pair. Further, we also show that adding our attack strategies during training helps alleviate the attacks. ## 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]()