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 …

This paper presents E5, a family of state-of-the-art text embeddings that transfer well to a
wide range of tasks. The model is trained in a contrastive manner with weak supervision …

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 …

Neural information retrieval (IR) has greatly advanced search and other knowledge-
intensive language tasks. While many neural IR methods encode queries and documents …

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 …

This paper introduces a simple yet effective query expansion approach, denoted as
query2doc, to improve both sparse and dense retrieval systems. The proposed method first …

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 …

In the era of large language models (LLMs), hallucination (ie, the tendency to generate
factually incorrect content) poses great challenge to trustworthy and reliable deployment of …

Managing the data for Information Retrieval (IR) experiments can be challenging. Dataset
documentation is scattered across the Internet and once one obtains a copy of the data …

We present a new zero-shot dense retrieval (ZeroDR) method, COCO-DR, to improve the
generalization ability of dense retrieval by combating the distribution shifts between source …