Robustness of Retrieval-Augmented Generation Systems to Adversarial Multimodal Queries
Abstract
This report synthesises findings from 8 peer-reviewed papers addressing the following research question: How robust are RAG systems to adversarial or out-of-distribution queries in multimodal settings (e.g., image+text benchmarks like VCR) when evaluated using metrics like BLEU or CHRF. 6 claims were extracted from source literature; 6 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 9.3/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: How robust are RAG systems to adversarial or out-of-distribution queries in multimodal settings (e.g., image+text benchmarks like VCR) when evaluated using metrics like BLEU or CHRF? Autonomous literature synthesis. Automated review score: 9.3/10. Full text and citation available at Assignee Research.
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.