ir_explain: a Python Library of Explainable IR Methods

While recent advancements in Neural Ranking Models have resulted in significant improvements over traditional statistical retrieval models, it is generally acknowledged that the use of large neural architectures and the application of complex language models in Information Retrieval (IR) have reduced the transparency of retrieval methods. Consequently, Explainability and Interpretability have emerged as important research topics in IR. Several axiomatic and post-hoc explanation methods, as well as approaches that attempt to be interpretable-by-design, have been proposed. This article presents \irexplain, an open-source Python library that implements a variety of well-known techniques for Explainable IR (ExIR) within a common, extensible framework. \irexplain supports the three standard categories of post-hoc explanations, namely pointwise, pairwise, and listwise explanations. The library is designed to make it easy to reproduce state-of-the-art ExIR baselines on standard test collections, as well as to explore new approaches to explaining IR models and methods. To facilitate adoption, \irexplain is well-integrated with widely-used toolkits such as Pyserini and \irdatasets.

@article{saha2025_2404.18546,
  title={ ir_explain: a Python Library of Explainable IR Methods },
  author={ Sourav Saha and Harsh Agarwal and Venktesh V and Avishek Anand and Swastik Mohanty and Debapriyo Majumdar and Mandar Mitra },
  journal={arXiv preprint arXiv:2404.18546},
  year={ 2025 }
}