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 …

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 …

Image-text retrieval (ITR) aims to retrieve images or texts that match a query originating from
the other modality. The conventional dense retrieval paradigm relies on encoding images …

Long document retrieval aims to fetch query-relevant documents from a large-scale
collection, where knowledge distillation has become de facto to improve a retriever by …

A ranker plays an indispensable role in the de facto'retrieval & rerank'pipeline, but its
training still lags behind--learning from moderate negatives or/and serving as an auxiliary …

Sparse representation learning based on Pre-trained Language Models has seen a growing
interest in Information Retrieval. Such approaches can take advantage of the proven …

Disentangled representation learning remains challenging as the underlying factors of
variation in the data do not naturally exist. The inherent complexity of real-world data makes …

The task of related work generation aims to generate a comprehensive survey of related
research topics automatically, saving time and effort for authors. Existing methods simplify …

Negative sampling stands as a pivotal technique in dense retrieval, essential for training
effective retrieval models and significantly impacting retrieval performance. While existing …

Generative retrieval shed light on a new paradigm of document retrieval, aiming to directly
generate the identifier of a relevant document for a query. While it takes advantage of …