Reranking is a critical stage in contemporary information retrieval (IR) systems, improving the relevance of the user-presented final results by honing initial candidate sets. This paper is a thorough guide to examine the changing reranker landscape and offer a clear view of the advancements made in reranking methods. We present a comprehensive survey of reranking models employed in IR, particularly within modern Retrieval Augmented Generation (RAG) pipelines, where retrieved documents notably influence output quality. We embark on a chronological journey through the historical trajectory of reranking techniques, starting with foundational approaches, before exploring the wide range of sophisticated neural network architectures such as cross-encoders, sequence-generation models like T5, and Graph Neural Networks (GNNs) utilized for structural information. Recognizing the computational cost of advancing neural rerankers, we analyze techniques for enhancing efficiency, notably knowledge distillation for creating competitive, lighter alternatives. Furthermore, we map the emerging territory of integrating Large Language Models (LLMs) in reranking, examining novel prompting strategies and fine-tuning tactics. This survey seeks to elucidate the fundamental ideas, relative effectiveness, computational features, and real-world trade-offs of various reranking strategies. The survey provides a structured synthesis of the diverse reranking paradigms, highlighting their underlying principles and comparative strengths and weaknesses.

翻译：重排序是当代信息检索（IR）系统中的关键阶段，通过优化初始候选集来提升呈现给用户的最终结果的相关性。本文旨在全面审视重排序模型的发展脉络，清晰呈现重排序方法所取得的进展。我们对信息检索中采用的重排序模型进行了系统综述，尤其聚焦于现代检索增强生成（RAG）流程，其中检索到的文档显著影响输出质量。我们按时间顺序追溯了重排序技术的历史轨迹，从基础方法入手，进而探讨了广泛应用的复杂神经网络架构，例如交叉编码器、序列生成模型（如T5）以及用于处理结构信息的图神经网络（GNN）。认识到先进神经重排序器的高计算成本，我们分析了提升效率的技术，特别是通过知识蒸馏来创建具有竞争力的轻量化替代方案。此外，我们描绘了将大型语言模型（LLM）集成到重排序中的新兴领域，研究了新颖的提示策略和微调方法。本综述旨在阐明各种重排序策略的基本思想、相对效能、计算特性及实际应用中的权衡取舍。通过对多样化重排序范式的结构化梳理，本文着重阐述了其核心原理与比较性优势及不足。