As a new paradigm in information retrieval, generative retrieval directly generates a ranked
list of document identifiers (docids) for a given query using generative language models …

Information Retrieval (IR) systems are crucial tools for users to access information, widely
applied in scenarios like search engines, question answering, and recommendation …

Popularized by the Differentiable Search Index, the emerging paradigm of generative
retrieval re-frames the classic information retrieval problem into a sequence-to-sequence …

Generative retrieval (GR) directly predicts the identifiers of relevant documents (ie, docids)
based on a parametric model. It has achieved solid performance on many ad-hoc retrieval …

Sequential recommendation is an important recommendation task that aims to predict the
next item in a sequence. Recently, adaptations of language models, particularly Transformer …

Abstract Differentiable Search Index is a recently proposed paradigm for document retrieval,
that encodes information about a corpus of documents within the parameters of a neural …

Building general-purpose robots that can operate seamlessly, in any environment, with any
object, and utilizing various skills to complete diverse tasks has been a long-standing goal in …

Generative information retrieval, encompassing two major tasks of Generative Document
Retrieval (GDR) and Grounded Answer Generation (GAR), has gained significant attention …

Model-based Information Retrieval (Model-based IR) has gained attention due to
advancements in generative language models. Unlike traditional dense retrieval methods …

Generative retrieval (GR) has become a highly active area of information retrieval (IR) that
has witnessed significant growth recently. Compared to the traditional “index-retrieve-then …