Text retrieval is a long-standing research topic on information seeking, where a system is
required to return relevant information resources to user's queries in natural language. From …

Dense retrievers have achieved state-of-the-art performance in various information retrieval
tasks, but to what extent can they be safely deployed in real-world applications? In this work …

Recent neural language models have taken a significant step forward in producing
remarkably controllable, fluent, and grammatical text. Although studies have found that AI …

Since ChatGPT has emerged as a major AIGC model, providing high-quality responses
across a wide range of applications (including software development and maintenance), it …

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

Artificial Intelligence (AI) systems such as autonomous vehicles, facial recognition, and
speech recognition systems are increasingly integrated into our daily lives. However …

Adversarial ranking attacks have gained increasing attention due to their success in probing
vulnerabilities, and, hence, enhancing the robustness, of neural ranking models …

Neural ranking models (NRMs) and dense retrieval (DR) models have given rise to
substantial improvements in overall retrieval performance. In addition to their effectiveness …

Reproducibility is crucial to the advancement of science; it strengthens confidence in
seemingly contradictory results and expands the boundaries of known discoveries …

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