5 citations · 8 across the 6 of their papers we have counts for
6 papers
Attack-in-the-Chain: Bootstrapping Large Language Models for Attacks Against Black-box Neural Ranking Models
Yu-An Liu, Ruqing Zhang, Jiafeng Guo +3
Neural ranking models (NRMs) have been shown to be highly effective in terms of retrieval performance. Unfortunately, they have also displayed a higher degree of sensitivity to att…
On the Robustness of Generative Information Retrieval Models
Yu-An Liu, Ruqing Zhang, Jiafeng Guo +3
Generative information retrieval methods retrieve documents by directly generating their identifiers. Much effort has been devoted to developing effective generative IR models. Les…
AI.vs.Clinician: Unveiling Intricate Interactions Between AI and Clinicians through an Open-Access Database
Wanling Gao, Yuan Liu, Zhuoming Yu +21
Artificial Intelligence (AI) plays a crucial role in medical field and has the potential to revolutionize healthcare practices. However, the success of AI models and their impacts…
Multi-granular Adversarial Attacks against Black-box Neural Ranking Models
Yu-An Liu, Ruqing Zhang, Jiafeng Guo +3
Adversarial ranking attacks have gained increasing attention due to their success in probing vulnerabilities, and, hence, enhancing the robustness, of neural ranking models. Conven…
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…
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…