Generative retrieval is a promising new neural retrieval paradigm that aims to optimize the retrieval pipeline by performing both indexing and retrieval with a single transformer model. However, this new paradigm faces challenges with updating the index and scaling to large collections. In this paper, we analyze two prominent variants of generative retrieval and show that they can be conceptually viewed as bi-encoders for dense retrieval. Specifically, we analytically demonstrate that the generative retrieval process can be decomposed into dot products between query and document vectors, similar to dense retrieval. This analysis leads us to propose a new variant of generative retrieval, called Tied-Atomic, which addresses the updating and scaling issues by incorporating techniques from dense retrieval. In experiments on two datasets, NQ320k and the full MSMARCO, we confirm that this approach does not reduce retrieval effectiveness while enabling the model to scale to large collections.

翻译：生成式检索是一种有前景的新型神经检索范式，旨在通过单个Transformer模型同时完成索引和检索，从而优化检索流程。然而，这一新范式在索引更新和大规模集合扩展方面面临挑战。本文分析了生成式检索的两种主要变体，并表明它们在概念上可被视为用于稠密检索的双编码器。具体而言，我们通过分析论证，生成式检索过程可分解为查询向量与文档向量之间的点积运算，这与稠密检索类似。这一分析促使我们提出生成式检索的新变体——Tied-Atomic，它通过整合稠密检索中的技术来解决更新与扩展问题。在NQ320k和完整MSMARCO两个数据集上的实验证实，该方法在保持检索有效性的同时，使模型能够扩展到大规模集合。