This paper reviews, analyzes, and proposes a new perspective on the bi-encoder architecture for neural search. While the bi-encoder architecture is widely used due to its simplicity and scalability at test time, it has some notable issues such as low performance on seen datasets and weak zero-shot performance on new datasets. In this paper, we analyze these issues and summarize two main critiques: the encoding information bottleneck problem and limitations of the basic assumption of embedding search. We then construct a thought experiment to logically analyze the encoding and searching operations and challenge the basic assumptions of embedding search. Building on these observations, we propose a new perspective on the bi-encoder architecture called the \textit{encoding--searching separation} perspective, which conceptually and practically separates the encoding and searching operations. This framework is applied to explain the root cause of existing issues and suggest mitigation strategies, potentially lowering training costs and improving retrieval performance. Finally, we discuss the broader implications of the ideas underlying this perspective, the new design surface it exposes, and potential research directions arising from it.

翻译：本文回顾、分析并提出了一种关于神经搜索中双编码器架构的新视角。尽管双编码器架构因其在测试时的简洁性和可扩展性而被广泛使用，但它存在一些显著问题，例如在已见数据集上性能较低，以及在新数据集上零样本性能较弱。本文分析了这些问题，并总结出两大主要批评：编码信息瓶颈问题以及嵌入搜索基本假设的局限性。随后，我们构建了一个思想实验，从逻辑上分析编码与搜索操作，并对嵌入搜索的基本假设提出挑战。基于这些观察，我们提出了一种称为\textit{编码-搜索分离}视角的双编码器架构新视角，该视角在概念和实践上将编码与搜索操作分离开来。应用此框架可以解释现有问题的根本原因并提出缓解策略，从而可能降低训练成本并提升检索性能。最后，我们讨论了该视角背后思想的更广泛影响、其所揭示的新设计层面，以及由此产生的潜在研究方向。