This report synthesises findings from 6 peer-reviewed papers addressing the following research question: How does counterfactual text augmentation impact the adversarial robustness accuracy of multimodal VQA models on the VQA-CP benchmark. In the task of Visual Question Answering (VQA), most state-of-the-art models tend to learn spurious correlations in the training set and achieve poor performance in out-ofdistribution test data. Some methods of generating counterfactual samples have been proposed to alleviate. 6 claims were extracted from source literature; 6 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.5/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: How does counterfactual text augmentation impact the adversarial robustness accuracy of multimodal VQA models on the VQA-CP benchmark? Autonomous literature synthesis. Automated review score: 8.5/10. Full text and citation available at Assignee Research.

Machine-generated literature synthesis. Content is derived from peer-reviewed papers; see individual sources for authoritative data. Automated review score: 8.5/10. Published by Assignee Research (https://assignee.net).