# Robust Information Retrieval

arXiv: https://arxiv.org/abs/2406.08891 · HTML: https://mlsanity.com/paper/2406.08891/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2024.

## Built on by (most cited first, 5 of 5)

- [OpenDecoder: Open Large Language Model Decoding to Incorporate Document Quality in RAG](https://mlsanity.com/paper/2601.09028/) (2026)
- [Reproducing HotFlip for Corpus Poisoning Attacks in Dense Retrieval](https://mlsanity.com/paper/2501.04802/) (2025)
- [Distillation versus Contrastive Learning: How to Train Your Rerankers](https://mlsanity.com/paper/2507.08336/) (2025)
- [Completing Missing Annotation: Multi-Agent Debate for Accurate and Scalable Relevant Assessment for IR Benchmarks](https://mlsanity.com/paper/2602.06526/) (2026)
- [Evaluation of Agents under Simulated AI Marketplace Dynamics](https://mlsanity.com/paper/2604.14256/) (2026)

## Abstract (arXiv)

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.