  Popularized by the Differentiable Search Index, the emerging paradigm of
generative retrieval re-frames the classic information retrieval problem into a
sequence-to-sequence modeling task, forgoing external indices and encoding an
entire document corpus within a single Transformer. Although many different
approaches have been proposed to improve the effectiveness of generative
retrieval, they have only been evaluated on document corpora on the order of
100k in size. We conduct the first empirical study of generative retrieval
techniques across various corpus scales, ultimately scaling up to the entire MS
MARCO passage ranking task with a corpus of 8.8M passages and evaluating model
sizes up to 11B parameters. We uncover several findings about scaling
generative retrieval to millions of passages; notably, the central importance
of using synthetic queries as document representations during indexing, the
ineffectiveness of existing proposed architecture modifications when accounting
for compute cost, and the limits of naively scaling model parameters with
respect to retrieval performance. While we find that generative retrieval is
competitive with state-of-the-art dual encoders on small corpora, scaling to
millions of passages remains an important and unsolved challenge. We believe
these findings will be valuable for the community to clarify the current state
of generative retrieval, highlight the unique challenges, and inspire new
research directions.
