Research Paper#Adversarial Robustness, Neural Ranking, Information Retrieval🔬 ResearchAnalyzed: Jan 3, 2026 16:08
RobustMask: Certified Robustness for Neural Ranking
Published:Dec 29, 2025 08:51
•1 min read
•ArXiv
Analysis
This paper addresses the critical vulnerability of neural ranking models to adversarial attacks, a significant concern for applications like Retrieval-Augmented Generation (RAG). The proposed RobustMask defense offers a novel approach combining pre-trained language models with randomized masking to achieve certified robustness. The paper's contribution lies in providing a theoretical proof of certified top-K robustness and demonstrating its effectiveness through experiments, offering a practical solution to enhance the security of real-world retrieval systems.
Key Takeaways
- •Proposes RobustMask, a novel defense against adversarial attacks on neural ranking models.
- •Combines pre-trained language models with randomized masking for robustness.
- •Provides a theoretical proof of certified top-K robustness.
- •Demonstrates effectiveness in certifying a significant portion of ranked documents against perturbations.
Reference
“RobustMask successfully certifies over 20% of candidate documents within the top-10 ranking positions against adversarial perturbations affecting up to 30% of their content.”