This paper outlines a conceptual framework for understanding recent developments in
information retrieval and natural language processing that attempts to integrate dense and …

Recent work has shown that incorporating explanations into the output generated by large
language models (LLMs) can significantly enhance performance on a broad spectrum of …

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

Recent work has shown that inducing a large language model (LLM) to generate
explanations prior to outputting an answer is an effective strategy to improve performance on …

This tutorial presents explainable information retrieval (ExIR), an emerging area focused on
fostering responsible and trustworthy deployment of machine learning systems in the context …

Understanding why a model makes certain predictions is crucial when adapting it for real
world decision making. LIME is a popular model-agnostic feature attribution method for the …

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

Neural retrieval models (NRMs) have been shown to outperform their statistical counterparts
owing to their ability to capture semantic meaning via dense document representations …

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

Explaining complex algorithms and models has recently received growing attention in
various domains to support informed decisions. Ranking functions are widely used for …