Robust Neural Information Retrieval: An Adversarial and Out-of-distribution Perspective (2407.06992v2)

Abstract: Recent advances in neural information retrieval (IR) models have significantly enhanced their effectiveness over various IR tasks. The robustness of these models, essential for ensuring their reliability in practice, has also garnered significant attention. With a wide array of research on robust IR being proposed, we believe it is the opportune moment to consolidate the current status, glean insights from existing methodologies, and lay the groundwork for future development. We view the robustness of IR to be a multifaceted concept, emphasizing its necessity against adversarial attacks, out-of-distribution (OOD) scenarios and performance variance. With a focus on adversarial and OOD robustness, we dissect robustness solutions for dense retrieval models (DRMs) and neural ranking models (NRMs), respectively, recognizing them as pivotal components of the neural IR pipeline. We provide an in-depth discussion of existing methods, datasets, and evaluation metrics, shedding light on challenges and future directions in the era of LLMs. To the best of our knowledge, this is the first comprehensive survey on the robustness of neural IR models, and we will also be giving our first tutorial presentation at SIGIR 2024 \url{https://sigir2024-robust-information-retrieval.github.io}. Along with the organization of existing work, we introduce a Benchmark for robust IR (BestIR), a heterogeneous evaluation benchmark for robust neural information retrieval, which is publicly available at \url{https://github.com/Davion-Liu/BestIR}. We hope that this study provides useful clues for future research on the robustness of IR models and helps to develop trustworthy search engines \url{https://github.com/Davion-Liu/Awesome-Robustness-in-Information-Retrieval}.

Insights into Robust Neural Information Retrieval: An Adversarial and OOD Perspective

This paper, authored by Yu-An Liu et al., presents a comprehensive survey on the robustness of neural information retrieval (IR) models, specifically emphasizing adversarial and out-of-distribution (OOD) robustness. The paper meticulously categorizes the robustness issues confronting IR models in terms of their vulnerability to adversarial attacks and their generalizability to OOD scenarios. This survey aims to underline the importance of robustness in deploying reliable and effective neural IR systems by providing a structured overview of current methodologies, datasets, and evaluation protocols.

The landscape of neural IR is undergoing rapid evolution, driven by the advances in deep learning. These models have demonstrated impressive effectiveness in learning query-document relevance patterns. However, the robustness of these models is of equal importance, particularly in scenarios involving adversarial attacks or exposure to OOD data. This paper identifies these challenges as multifaceted, emphasizing both adversarial and OOD robustness as key dimensions in which IR models need to excel to ensure reliability and utility in practical applications.

The paper delineates its exploration into adversarial robustness by differentiating between adversarial ranking and retrieval attacks. Adversarial ranking attacks primarily target the re-ranking phase of IR, intending to manipulate the order of results by exploiting the neural ranking models (NRMs). Conversely, adversarial retrieval attacks focus on the retrieval phase, targeting dense retrieval models (DRMs) to influence which documents are recalled from the corpus. The paper also reviews defense strategies, categorizing them into attack detection, empirical defenses, and certified robustness. The authors meticulously compile various methodologies designed to fortify models against adversarial vulnerabilities, promising advancements such as perturbation-invariant adversarial training and certified defenses to prevent adversarial manipulation.

On the OOD robustness front, the authors examine the ability of IR models to generalize effectively across unseen documents and queries. With a structured taxonomy, the paper highlights techniques for adaptation to new corpora, continuous corpus updates, and handling query variations. The challenges of adapting models to domain shifts are underscored as critical to deploying IR systems that maintain performance over time in the face of evolving corpus databases and user query profiles. Techniques such as data augmentation, continual learning, and domain-invariant projections are explored to bridge the gap between seen and unseen data distributions, thus enhancing robustness.

The paper's strength lies in its thoroughness in documenting and organizing existing research in IR robustness, providing a rich resource for researchers engaged in this domain. The authors also offer valuable insights into the selection and utilization of datasets and benchmarks, which are critical to advancing robustness assessments in neural IR. By providing a curated list of resources, including the BestIR benchmark, the authors extend practical tools for the community to facilitate robust IR model development and evaluation.

Despite this comprehensive survey, the authors acknowledge persistent open challenges. These include addressing reliance on large-scale data and enhancing the generalizability of NRMs. The paper suggests future research directions to tackle issues such as penetration attacks, universal attacks, and defense in practice, which remain unresolved and exigent areas in the advancement of robust neural IR models.

As neural IR continues its trajectory toward greater adoption and adaptability, this survey provides an essential foundation for understanding and improving robustness in neural IR models. The paper's call to bridge the gap between model effectiveness and robustness without compromising the dynamic nature of IR systems is a noteworthy pursuit that can inspire ongoing research and innovation in developing advanced, dependable, and secure IR models.