Adversarial queries in information retrieval (IR) are intentionally designed to confuse or mislead the retrieval system. To handle such queries, IR systems often rely on robust ranking and filtering techniques that can detect and mitigate suspicious patterns. This could involve using deep learning models trained to recognize adversarial manipulation or filtering out anomalous queries based on known attack patterns.
Another strategy is to introduce redundancy and diversity in the search results, ensuring that the system is less sensitive to specific adversarial manipulations. By ranking multiple diverse sources or using ensemble methods, IR systems can reduce the impact of adversarial queries on the overall result quality.
Furthermore, continuous monitoring and retraining of IR models, incorporating adversarial examples during training, can help improve their resilience to adversarial attacks over time.