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 …

It has been shown that the interpretability of search results is enhanced when query aspects
covered by documents are explicitly provided. However, existing work on aspect-oriented …

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 …

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

Recently substantial improvements in neural retrieval methods also bring to light the
inherent blackbox nature of these methods, especially when viewed from an explainability …

Introduction People are today increasingly relying on health information they find online to
make decisions that may impact both their physical and mental wellbeing. Therefore, there is …

Search Result Explanation (SeRE) aims to improve search sessions' effectiveness and
efficiency by helping users interpret documents' relevance. Existing works mostly focus on …

The process of scale calibration in ranking systems involves adjusting the outputs of rankers
to correspond with significant qualities like click-through rates or relevance, crucial for …

We address some of the limitations of coverage-based search result diversification models,
which often consist of separate components and rely on external systems for query aspects …