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

Knowledge retrieval with multi-modal queries plays a crucial role in supporting knowledge-
intensive multi-modal applications. However, existing methods face challenges in terms of …

With the information explosion on the Web, search and recommendation are foundational
infrastructures to satisfying users' information needs. As the two sides of the same coin, both …

Recent research has shown that transformer networks can be used as differentiable search
indexes by representing each document as a sequence of document ID tokens. These …

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 …

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 …

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 …

This paper introduces PAG-a novel optimization and decoding approach that guides
autoregressive generation of document identifiers in generative retrieval models through …

Recently, a novel generative retrieval (GR) paradigm has been proposed, where a single
sequence-to-sequence model is learned to directly generate a list of relevant document …

Large language models (LLMs) have gained significant attention in various fields but prone
to hallucination, especially in knowledge-intensive (KI) tasks. To address this, retrieval …