The period from 2019 to the present marks one of the most significant paradigm shifts in information retrieval (IR) and natural language processing (NLP), culminating in the emergence of powerful large language models (LLMs) from 2022 onward. Methods based on pretrained encoder-only architectures (e.g., BERT) as well as decoder-only generative LLMs have outperformed many earlier approaches, demonstrating particularly strong performance in zero-shot scenarios and complex reasoning tasks. This survey examines the evolution of model architectures in IR, with a focus on two key aspects: backbone models for feature extraction and end-to-end system architectures for relevance estimation. To maintain analytical clarity, we deliberately separate architectural design from training methodologies, enabling a focused examination of structural innovations in IR systems. We trace the progression from traditional term-based retrieval models to modern neural approaches, highlighting the transformative impact of transformer-based architectures and subsequent LLM developments. The survey concludes with a forward-looking discussion of open challenges and emerging research directions, including architectural optimization for efficiency and scalability, robust handling of multimodal and multilingual data, and adaptation to novel application domains such as autonomous search agents, which may represent the next paradigm in IR.

翻译：2019年至今标志着信息检索（IR）与自然语言处理（NLP）领域经历了最重大的范式转变之一，并在2022年后催生了强大的大语言模型（LLMs）。基于预训练仅编码器架构（如BERT）的方法以及仅解码器生成式大语言模型已超越了许多早期方法，在零样本场景和复杂推理任务中展现出尤为卓越的性能。本综述审视了信息检索中模型架构的演进，重点关注两个关键方面：用于特征提取的骨干模型以及用于相关性估计的端到端系统架构。为保持分析的清晰性，我们有意将架构设计与训练方法分离，从而聚焦于信息检索系统的结构创新。我们追溯了从传统的基于词项的检索模型到现代神经方法的演进历程，强调了基于Transformer的架构及后续大语言模型发展所带来的变革性影响。综述最后对开放挑战与新兴研究方向进行了前瞻性讨论，包括面向效率与可扩展性的架构优化、对多模态与多语言数据的鲁棒处理，以及向自主搜索代理等新型应用领域的适配，这些可能代表了信息检索的下一个范式。