7 citations · 18 across the 17 of their papers we have counts for
4 papers · 1 filter
Perturbation-Invariant Adversarial Training for Neural Ranking Models: Improving the Effectiveness-Robustness Trade-Off
Yu-An Liu, Ruqing Zhang, Mingkun Zhang +4
Neural ranking models (NRMs) have shown great success in information retrieval (IR). But their predictions can easily be manipulated using adversarial examples, which are crafted b…
Black-box Adversarial Attacks against Dense Retrieval Models: A Multi-view Contrastive Learning Method
Yu-An Liu, Ruqing Zhang, Jiafeng Guo +4
Neural ranking models (NRMs) and dense retrieval (DR) models have given rise to substantial improvements in overall retrieval performance. In addition to their effectiveness, and m…
On the Robustness of Generative Retrieval Models: An Out-of-Distribution Perspective
Yu-An Liu, Ruqing Zhang, Jiafeng Guo +2
Recently, we have witnessed generative retrieval increasingly gaining attention in the information retrieval (IR) field, which retrieves documents by directly generating their iden…
Topic-oriented Adversarial Attacks against Black-box Neural Ranking Models
Yu-An Liu, Ruqing Zhang, Jiafeng Guo +4
Neural ranking models (NRMs) have attracted considerable attention in information retrieval. Unfortunately, NRMs may inherit the adversarial vulnerabilities of general neural netwo…