  Generative Retrieval (GR), autoregressively decoding relevant document
identifiers given a query, has been shown to perform well under the setting of
small-scale corpora. By memorizing the document corpus with model parameters,
GR implicitly achieves deep interaction between query and document. However,
such a memorizing mechanism faces three drawbacks: (1) Poor memory accuracy for
fine-grained features of documents; (2) Memory confusion gets worse as the
corpus size increases; (3) Huge memory update costs for new documents. To
alleviate these problems, we propose the Generative Dense Retrieval (GDR)
paradigm. Specifically, GDR first uses the limited memory volume to achieve
inter-cluster matching from query to relevant document clusters.
Memorizing-free matching mechanism from Dense Retrieval (DR) is then introduced
to conduct fine-grained intra-cluster matching from clusters to relevant
documents. The coarse-to-fine process maximizes the advantages of GR's deep
interaction and DR's scalability. Besides, we design a cluster identifier
constructing strategy to facilitate corpus memory and a cluster-adaptive
negative sampling strategy to enhance the intra-cluster mapping ability.
Empirical results show that GDR obtains an average of 3.0 R@100 improvement on
NQ dataset under multiple settings and has better scalability.
