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 embeddings are useful features in many applications such as semantic search and
computing text similarity. Previous work typically trains models customized for different use …

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

It has been shown that dual encoders trained on one domain often fail to generalize to other
domains for retrieval tasks. One widespread belief is that the bottleneck layer of a dual …

Much recent research on information retrieval has focused on how to transfer from one task
(typically with abundant supervised data) to various other tasks where supervision is limited …

In this paper, we comprehensively investigate the potential misuse of modern Large
Language Models (LLMs) for generating credible-sounding misinformation and its …

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 …

Abstract Knowledge-intensive language tasks require NLP systems to both provide the
correct answer and retrieve supporting evidence for it in a given corpus. Autoregressive …

Conversational information seeking (CIS) is concerned with a sequence of interactions
between one or more users and an information system. Interactions in CIS are primarily …

Abstract Evaluation in Information Retrieval (IR) relies on post-hoc empirical procedures,
which are time-consuming and expensive operations. To alleviate this, Query Performance …