This paper provides an extensive and thorough overview of the models and techniques
utilized in the first and second stages of the typical information retrieval processing chain …

As we transition from the 5G epoch, a new horizon beckons with the advent of 6G, seeking a
profound fusion with novel communication paradigms and emerging technological trends …

Conducting text retrieval in a dense learned representation space has many intriguing
advantages over sparse retrieval. Yet the effectiveness of dense retrieval (DR) often requires …

In various natural language processing tasks, passage retrieval and passage re-ranking are
two key procedures in finding and ranking relevant information. Since both the two …

In open-domain question answering, dense passage retrieval has become a new paradigm
to retrieve relevant passages for finding answers. Typically, the dual-encoder architecture is …

Recent progress in Natural Language Understanding (NLU) is driving fast-paced advances
in Information Retrieval (IR), largely owed to fine-tuning deep language models (LMs) for …

The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in
response to a query. Although the most common formulation of text ranking is search …

This work proposes a novel adaptation of a pretrained sequence-to-sequence model to the
task of document ranking. Our approach is fundamentally different from a commonly …

Classical information retrieval systems such as BM25 rely on exact lexical match and carry
out search efficiently with inverted list index. Recent neural IR models shifts towards soft …

We study the problem of retrieval with instructions, where users of a retrieval system
explicitly describe their intent along with their queries. We aim to develop a general-purpose …