  Generative Retrieval (GR) is an emerging paradigm in information retrieval
that leverages generative models to directly map queries to relevant document
identifiers (DocIDs) without the need for traditional query processing or
document reranking. This survey provides a comprehensive overview of GR,
highlighting key developments, indexing and retrieval strategies, and
challenges. We discuss various document identifier strategies, including
numerical and string-based identifiers, and explore different document
representation methods. Our primary contribution lies in outlining future
research directions that could profoundly impact the field: improving the
quality of query generation, exploring learnable document identifiers,
enhancing scalability, and integrating GR with multi-task learning frameworks.
By examining state-of-the-art GR techniques and their applications, this survey
aims to provide a foundational understanding of GR and inspire further
innovations in this transformative approach to information retrieval. We also
make the complementary materials such as paper collection publicly available at
https://github.com/MiuLab/GenIR-Survey/
