mlsanityData to 7 Oct 2026

A Survey of Generative Information Retrieval

arXiv 2406.01197 ↗First posted 3 Jun 2024cs.IR cs.CLcited by 4

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Abstract (arXiv)

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/

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 41 of its 54 references that are in the map. First posted on arXiv 3 Jun 2024.

Builds on

41 of 54 references.

References · 41

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29 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.

  1. 2025Knowledge Graph-Guided Retrieval Augmented Generationcited by 57
  2. 2024Generative Retrieval with Few-shot Indexingcited by 3
  3. 2025Purely Semantic Indexing for LLM-based Generative Recommendation and Retrievalcited by 2
  4. 2026Preference Shapes Relevance: Cross-component Hierarchical Semantic Alignment for Personalized Generative Retrievalcited by 0