Text retrieval is a long-standing research topic on information seeking, where a system is
required to return relevant information resources to user's queries in natural language. From …

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

Knowledge-intensive language tasks (KILT) usually require a large body of information to
provide correct answers. A popular paradigm to solve this problem is to combine a search …

Knowledge-intensive language tasks (KILTs) benefit from retrieving high-quality relevant
contexts from large external knowledge corpora. Learning task-specific retrievers that return …

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 …

Recently, a new paradigm called Differentiable Search Index (DSI) has been proposed for
document retrieval, wherein a sequence-to-sequence model is learned to directly map …

The recent advent of end-to-end generative retrieval marks a significant shift in document
retrieval methods, leveraging differentiable search indexes to directly produce relevant …

Document retrieval has been extensively studied within the index-retrieve framework for
decades, which has withstood the test of time. Unfortunately, such a pipelined framework …

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 …