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 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 work, we propose a novel framework to devise features that can be used by Query
Performance Prediction (QPP) models for Neural Information Retrieval (NIR). Using the …

As information retrieval researchers, we not only develop algorithmic solutions to hard
problems, but we also insist on a proper, multifaceted evaluation of ideas. The literature on …

Prompt tuning attempts to update few task-specific parameters in pre-trained models. It has
achieved comparable performance to fine-tuning of the full parameter set on both language …

Recent years have witnessed the significant advance in dense retrieval (DR) based on
powerful pre-trained language models (PLM). DR models have achieved excellent …

We study hybrid search in text retrieval where lexical and semantic search are fused
together with the intuition that the two are complementary in how they model relevance. In …

Recent advances in neural information retrieval (IR) models have significantly enhanced
their effectiveness over various IR tasks. The robustness of these models, essential for …

Maximum inner product search (MIPS) over dense and sparse vectors have progressed
independently in a bifurcated literature for decades; the latter is better known as top-retrieval …

Pseudo-relevance feedback (PRF) is a classical technique to improve search engine
retrieval effectiveness, by closing the vocabulary gap between users' query formulations and …