This paper introduces PAG-a novel optimization and decoding approach that
guides autoregressive generation of document identifiers in generative
retrieval models through simultaneous decoding. To this aim, PAG constructs a
set-based and sequential identifier for each document. Motivated by the
bag-of-words assumption in information retrieval, the set-based identifier is
built on lexical tokens. The sequential identifier, on the other hand, is
obtained via quantizing relevance-based representations of documents. Extensive
experiments on MSMARCO and TREC Deep Learning Track data reveal that PAG
outperforms the state-of-the-art generative retrieval model by a large margin
(e.g., 15.6% MRR improvements on MS MARCO), while achieving 22x speed up in
terms of query latency.

本文介绍了 PAG，一种新的优化和解码方法，通过同时解码来指导生成式检索模型中文档标识符的自回归生成。PAG 为每个文档构建了一种基于集合和顺序的标识符，其中基于集合的标识符基于词汇标记建立，而顺序标识符则通过量化文档的基于相关性的表示获得。在 MSMARCO 和 TREC 深度学习赛道数据上进行的大量实验证明 PAG 在性能上超过了现有的生成式检索模型（例如，在 MS MARCO 上提高了 15.6% 的 MRR），同时查询延迟方面达到了 22 倍的加速。