  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 generative language models, distinct from
traditional sparse or dense retrieval methods. In this work, we identify a
viable direction to further enhance generative retrieval via distillation and
propose a feasible framework, named DGR. DGR utilizes sophisticated ranking
models, such as the cross-encoder, in a teacher role to supply a passage rank
list, which captures the varying relevance degrees of passages instead of
binary hard labels; subsequently, DGR employs a specially designed distilled
RankNet loss to optimize the generative retrieval model, considering the
passage rank order provided by the teacher model as labels. This framework only
requires an additional distillation step to enhance current generative
retrieval systems and does not add any burden to the inference stage. We
conduct experiments on four public datasets, and the results indicate that DGR
achieves state-of-the-art performance among the generative retrieval methods.
Additionally, DGR demonstrates exceptional robustness and generalizability with
various teacher models and distillation losses.
