We’re on a journey to advance and democratize artificial intelligence through open source and open science.

It can be easy to forget, or look away from, the pain and disruption of the pandemic. The numbers will be there to remind us.

A wide range of transformer-based language models have been proposed for information retrieval tasks. However, including transformer-based models in retrieval pipelines is often complex and requires substantial engineering effort. In this paper, we introduce Lightning IR, an easy-to-use PyTorch Lightning-based framework for applying transformer-based language models in retrieval scenarios. Lightning IR provides a modular and extensible architecture that supports all stages of a retrieval pipeline: from fine-tuning and indexing to searching and re-ranking. Designed to be scalable and reproducible, Lightning IR is available as open-source: https://github.com/webis-de/lightning-ir.

The SIGIR 2025 keynotes are held by esteemed speakers: Robertson S., Gurevych I. and Frieder O., who will cover topics that range from AI in medical search and ecommendation to BM25 and probabilistic ...