Robust Neural Information Retrieval: An Adversarial and Out-of-distribution Perspective

According to the global overview report from Digital 2023, nearly 82% of Internet users between 18 and 64 have used a search engine or web portal in the past month. Specifically, IR is the process of finding and providing relevant information in response to the user query from a large collection of data. Recently, with advances in deep learning, neural IR models have witnessed significant progress [51, 53]. With the development of training methodologies such as pre-training [44, 100] and fine-tuning [73, 117, 162], neural IR models have demonstrated remarkable effectiveness in learning query-document relevance patterns. Why is robustness important in IR? In real-world deployment of neural IR models, an aspect equally essential as their effectiveness is their robustness. A good IR system must not only exhibit high effectiveness under normal conditions but also demonstrate robustness in the face of abnormal conditions. The natural openness of IR systems makes them vulnerable to intrusion, and the consequences can be severe. For example: (i) Search engines are vulnerable to black hat SEO attacks, necessitating significant efforts to curb these infringements.