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 …

Recent years have seen impressive performance of transformer-based models on different
natural language processing tasks. However, it is not clear to what degree the transformers …

Large language models have shown impressive few-shot results on a wide range of tasks.
However, when knowledge is key for such results, as is the case for tasks such as question …

Retrieval-Augmented Generation (RAG) has recently emerged as a method to extend
beyond the pre-trained knowledge of Large Language Models by augmenting the original …

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 …

A network intrusion detection system is critical for cyber security against illegitimate attacks.
In terms of feature perspectives, network traffic may include a variety of elements such as …

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 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 …

Current query expansion models use pseudo-relevance feedback to improve first-pass
retrieval effectiveness; however, this fails when the initial results are not relevant. Instead of …

The core of information retrieval (IR) is to identify relevant information from large-scale
resources and return it as a ranked list to respond to user's information need. In recent years …