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 …

Various techniques have been developed in recent years to improve dense retrieval (DR),
such as unsupervised contrastive learning and pseudo-query generation. Existing DRs …

Pre-trained language models are increasingly important components across multiple
information retrieval (IR) paradigms. Late interaction, introduced with the ColBERT model …

Dense retrieval (DR) approaches based on powerful pre-trained language models (PLMs)
achieved significant advances and have become a key component for modern open-domain …

Pre-trained language models have been successful in many knowledge-intensive NLP
tasks. However, recent work has shown that models such as BERT are not “structurally …

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 …

Retrieval models based on dense representations in semantic space have become an
indispensable branch for first-stage retrieval. These retrievers benefit from surging advances …

Recent recommender systems have shown remarkable performance by using an ensemble
of heterogeneous models. However, it is exceedingly costly because it requires resources …

Recent rapid advances in deep pre-trained language models and the introduction of large
datasets have powered research in embedding-based neural retrieval. While many …

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 …