Structured representation for Information Retrieval - Sorbonne Université
Conference Papers Year : 2024

Structured representation for Information Retrieval

Abstract

Generative information retrieval uses transformer neural networks as differential search indexes, representing them as a sequence of document identifier tokens. Some of the work propose to use arbitrary identifiers while other works propose to use meta-data (text, URL, title) as identifiers. We propose a new generative approach, named REFERENTIAL, that combines these two directions: using prefix-biased identifiers and removing the one-to-one relationship between an identifier and a document. This paper gives the brief introduction of my thesis and what I have done during the past year.
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Dates and versions

hal-04788243 , version 1 (18-11-2024)

Identifiers

Cite

Yuxuan Zong, Benjamin Piwowarski. Structured representation for Information Retrieval. COnférence en Recherche d'Informations et Applications, Apr 2024, La Rochelle, France. ⟨10.24348/coria.2024.abstract_24⟩. ⟨hal-04788243⟩
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