7 citations · 18 across the 17 of their papers we have counts for
5 papers · 1 filter
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…
Robust Neural Information Retrieval: An Adversarial and Out-of-distribution Perspective
Yu-An Liu, Ruqing Zhang, Jiafeng Guo +3
Recent advances in neural information retrieval (IR) models have significantly enhanced their effectiveness over various IR tasks. The robustness of these models, essential for ens…
Robust Information Retrieval
Yu-An Liu, Ruqing Zhang, Jiafeng Guo +1
Beyond effectiveness, the robustness of an information retrieval (IR) system is increasingly attracting attention. When deployed, a critical technology such as IR should not only d…
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…