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 strong performance on average but also have the ability to handle a variety of exceptional situations. In recent years, research into the robustness of IR has seen significant growth, with numerous researchers offering extensive analyses and proposing myriad strategies to address robustness challenges. In this tutorial, we first provide background information covering the basics and a taxonomy of robustness in IR. Then, we examine adversarial robustness and out-of-distribution (OOD) robustness within IR-specific contexts, extensively reviewing recent progress in methods to enhance robustness. The tutorial concludes with a discussion on the robustness of IR in the context of large language models (LLMs), highlighting ongoing challenges and promising directions for future research. This tutorial aims to generate broader attention to robustness issues in IR, facilitate an understanding of the relevant literature, and lower the barrier to entry for interested researchers and practitioners.

翻译：除了有效性之外，信息检索系统的鲁棒性正日益受到关注。当部署IR这类关键技术时，它不仅应在平均情况下表现出强劲性能，还应具备处理各种异常情况的能力。近年来，IR鲁棒性研究取得了显著增长，众多研究者提供了广泛的分析，并提出了大量应对鲁棒性挑战的策略。在本教程中，我们首先提供涵盖IR鲁棒性基础知识和分类的背景信息。接着，我们在IR特定背景下审视对抗鲁棒性和分布外鲁棒性，广泛回顾了增强鲁棒性方法的最新进展。教程最后讨论了大型语言模型背景下的IR鲁棒性，重点指出了当前面临的挑战和未来研究的有前景方向。本教程旨在引起对IR鲁棒性问题的更广泛关注，促进对相关文献的理解，并为感兴趣的研究者和实践者降低入门门槛。