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 …

Existing neural information retrieval (IR) models have often been studied in homogeneous
and narrow settings, which has considerably limited insights into their out-of-distribution …

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 …

In this paper, we propose SimLM (Similarity matching with Language Model pre-training), a
simple yet effective pre-training method for dense passage retrieval. It employs a simple …

The information retrieval community has recently witnessed a revolution due to large
pretrained transformer models. Another key ingredient for this revolution was the MS …

The MS MARCO ranking dataset has been widely used for training deep learning models for
IR tasks, achieving considerable effectiveness on diverse zero-shot scenarios. However, this …

Scaling laws have been observed in a wide range of tasks, particularly in language
generation. Previous studies have found that the performance of large language models …

Neural retrievers based on pre-trained language models (PLMs), such as dual-encoders,
have achieved promising performance on the task of open-domain question answering …

We propose end-to-end multimodal fact-checking and explanation generation, where the
input is a claim and a large collection of web sources, including articles, images, videos, and …

Despite their recent popularity and well-known advantages, dense retrievers still lag behind
sparse methods such as BM25 in their ability to reliably match salient phrases and rare …