Neural ranking models (NRMs) have shown remarkable success in recent years, especially
with pre-trained language models. However, deep neural models are notorious for their …

Neural ranking models (NRMs) have attracted considerable attention in information retrieval.
Unfortunately, NRMs may inherit the adversarial vulnerabilities of general neural networks …

Neural text ranking models have witnessed significant advancement and are increasingly
being deployed in practice. Unfortunately, they also inherit adversarial vulnerabilities of …

Explainable information retrieval is an emerging research area aiming to make transparent
and trustworthy information retrieval systems. Given the increasing use of complex machine …

Recent advances in neural information retrieval (IR) models have significantly enhanced
their effectiveness over various IR tasks. The robustness of these models, essential for …

Adversarial attacks have gained traction in order to identify potential vulnerabilities in neural
ranking models (NRMs), but current attack methods often introduce grammatical errors …

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 deliver …

The Web is a canonical example of a competitive search setting that includes document
authors with ranking incentives: their goal is to promote their documents in rankings induced …

The pre-trained language models (PLMs), such as BERT, have been successfully employed
in two-phases ranking pipeline for information retrieval (IR). Meanwhile, recent studies have …

Neural ranking models (NRMs) have achieved promising results in information retrieval.
NRMs have also been shown to be vulnerable to adversarial examples. A typical Word …