# Model Agnostic Answer Reranking System for Adversarial Question Answering Source: [Model Agnostic Answer Reranking System for Adversarial Question Answering]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:56:51.523Z Authors: Sagnik Majumder, Chinmoy Samant, Greg Durrett Published: 2021-02-05T06:18:12.000Z ## Abstract While numerous methods have been proposed as defenses against adversarial examples in question answering (QA), these techniques are often model specific, require retraining of the model, and give only marginal improvements in performance over vanilla models. In this work, we present a simple model-agnostic approach to this problem that can be applied directly to any QA model without any retraining. Our method employs an explicit answer candidate reranking mechanism that scores candidate answers on the basis of their content overlap with the question before making the final prediction. Combined with a strong base QAmodel, our method outperforms state-of-the-art defense techniques, calling into question how well these techniques are actually doing and strong these adversarial testbeds are. ## 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]()