Generative retrieval is a promising new paradigm in text retrieval that
generates identifier strings of relevant passages as the retrieval target. This
paradigm leverages powerful generation models and represents a new paradigm
distinct from traditional learning-to-rank methods. However, despite its rapid
development, current generative retrieval methods are still limited. They
typically rely on a heuristic function to transform predicted identifiers into
a passage rank list, which creates a gap between the learning objective of
generative retrieval and the desired passage ranking target. Moreover, the
inherent exposure bias problem of text generation also persists in generative
retrieval. To address these issues, we propose a novel framework, called LTRGR,
that combines generative retrieval with the classical learning-to-rank
paradigm. Our approach involves training an autoregressive model using a
passage rank loss, which directly optimizes the autoregressive model toward the
optimal passage ranking. This framework only requires an additional training
step to enhance current generative retrieval systems and does not add any
burden to the inference stage. We conducted experiments on three public
datasets, and our results demonstrate that LTRGR achieves state-of-the-art
performance among generative retrieval methods, indicating its effectiveness
and robustness.

提出了一个称为 LTRGR 的新框架，结合了生成式检索和经典的学习 - 排序模型，通过使用段落排名损失训练自回归模型，只需要额外的训练步骤即可增强当前的生成式检索系统，并在三个公共数据集上实现了最先进的性能。