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
this https URL

通过综述 Generative Retrieval（GR）的关键发展、索引和检索策略和挑战，讨论了具有挑战性的生成查询质量、可学习文档标识符、可扩展性和多任务学习框架等未来研究方向，旨在为 GR 提供基础理解并激发信息检索领域的进一步创新。

生成式信息检索综述

A Survey of Generative Information Retrieval

Information retrieval (IR) for precision medicine (PM) often involves looking
for multiple pieces of evidence that characterize a patient case. This
typically includes at least the name of a condition and a genetic variation
that applies to the patient. Other factors such as demographic attributes,
comorbidities, and social determinants may also be pertinent. As such, the
retrieval problem is often formulated as ad hoc search but with multiple facets
(e.g., disease, mutation) that may need to be incorporated. In this paper, we
present a document reranking approach that combines neural query-document
matching and text summarization toward such retrieval scenarios. Our
architecture builds on the basic BERT model with three specific components for
reranking: (a). document-query matching (b). keyword extraction and (c).
facet-conditioned abstractive summarization. The outcomes of (b) and (c) are
used to essentially transform a candidate document into a concise summary that
can be compared with the query at hand to compute a relevance score. Component
(a) directly generates a matching score of a candidate document for a query.
The full architecture benefits from the complementary potential of
document-query matching and the novel document transformation approach based on
summarization along PM facets. Evaluations using NIST's TREC-PM track datasets
(2017--2019) show that our model achieves state-of-the-art performance. To
foster reproducibility, our code is made available here:
this https URL

本文介绍了一种基于文档重排序的方法，结合神经查询 - 文档匹配和文本摘要技术，用于信息检索和精准医疗中多方面的搜索问题，并在 NIST TREC-PM 轨迹数据集上取得了最新的表现。