Neural retrievers based on dense representations combined with Approximate Nearest
Neighbors search have recently received a lot of attention, owing their success to distillation …

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 …

Explainable information retrieval is an emerging research area aiming to make transparent
and trustworthy information retrieval systems. Given the increasing use of complex machine …

Neural models have demonstrated remarkable performance across diverse ranking tasks.
However, the processes and internal mechanisms along which they determine relevance …

Sparse representation learning based on Pre-trained Language Models has seen a growing
interest in Information Retrieval. Such approaches can take advantage of the proven …

The late interaction paradigm introduced with ColBERT stands out in the neural Information
Retrieval space, offering a compelling effectiveness-efficiency trade-off across many …

Dense multi-representation retrieval models, exemplified as ColBERT, estimate the
relevance between a query and a document based on the similarity of their contextualised …

ranxhub is an online repository for sharing artifacts deriving from the evaluation of
Information Retrieval systems. Specifically, we provide a platform for sharing pre-computed …

Dual encoders are highly effective and widely deployed in the retrieval phase for passage
and document ranking, question answering, or retrieval-augmented generation (RAG) …

Using large language models (LMs) for query or document expansion can improve
generalization in information retrieval. However, it is unknown whether these techniques are …