As a primary means of information acquisition, information retrieval (IR) systems, such as
search engines, have integrated themselves into our daily lives. These systems also serve …

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

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 …

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 …

Recent advances in large language models have enabled the development of viable
generative retrieval systems. Instead of a traditional document ranking, generative retrieval …

Pre-trained Transformers (eg, BERT) have been commonly used in existing dense retrieval
methods for parameter initialization, and recent studies are exploring more effective pre …

For a given query generative retrieval generates identifiers of relevant documents in an end-
to-end manner using a sequence-to-sequence architecture. The relation between …

Recently, generative retrieval has emerged as a promising alternative to the traditional
retrieval paradigms. It assigns each document a unique identifier, known as the DocID, and …

Documents come in all shapes and sizes and are created by many different means,
including now-a-days, generative language models. We demonstrate that a simple genetic …