Auto-regressive search engines emerge as a promising paradigm for next-gen information retrieval systems. These methods work with Seq2Seq models, where each query can be directly mapped to the identifier of its relevant document. As such, they are praised for merits like being end-to-end differentiable. However, auto-regressive search engines also confront challenges in retrieval quality, given the requirement for the exact generation of the document identifier. That's to say, the targeted document will be missed from the retrieval result if a false prediction about its identifier is made in any step of the generation process. In this work, we propose a novel framework, namely AutoTSG (Auto-regressive Search Engine with Term-Set Generation), which is featured by 1) the unordered term-based document identifier and 2) the set-oriented generation pipeline. With AutoTSG, any permutation of the term-set identifier will lead to the retrieval of the corresponding document, thus largely relaxing the requirement of exact generation. Besides, the Seq2Seq model is enabled to flexibly explore the optimal permutation of the document identifier for the presented query, which may further contribute to the retrieval quality. AutoTSG is empirically evaluated with Natural Questions and MS MARCO, where notable improvements can be achieved against the existing auto-regressive search engines.

翻译：自回归搜索引擎作为下一代信息检索系统的一种有前景范式而出现。这些方法采用序列到序列（Seq2Seq）模型，其中每个查询可以直接映射到其相关文档的标识符。为此，它们因具有端到端可微分等优点而受到赞誉。然而，由于需要精确生成文档标识符，自回归搜索引擎在检索质量方面也面临挑战。也就是说，如果在生成过程的任何步骤中对文档标识符做出错误预测，目标文档就会从检索结果中遗漏。在本工作中，我们提出了一种新颖框架，即AutoTSG（基于术语集生成的自回归搜索引擎），其特点在于：1）无序的基于术语的文档标识符，以及2）面向集合的生成流程。借助AutoTSG，术语集标识符的任何排列都将导致对应文档的检索，从而大大放宽了对精确生成的要求。此外，Seq2Seq模型能够灵活探索针对给定查询的文档标识符的最优排列，这可能进一步有助于提高检索质量。我们利用Natural Questions和MS MARCO数据集对AutoTSG进行了实证评估，结果表明，与现有自回归搜索引擎相比，该方法能够取得显著改进。