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ir_explain: a Python Library of Explainable IR Methods

Sourav Saha, Harsh Agarwal, V Venktesh, Avishek Anand, Swastik Mohanty, Debapriyo Majumdar, Mandar Mitra

TL;DR

The paper introduces ir_explain, an open-source Python library that unifies major Explainable IR (ExIR) methods across post-hoc (pointwise, pairwise, listwise) and interpretable-by-design paradigms, with integrated support for common IR toolkits and datasets. It outlines a modular architecture comprising three explainer families, probing, evaluation, visualization, and utilities to reproduce state-of-the-art baselines and experiment with new approaches. Through demonstrations on robustness, ranking-approximation, replicability, and RAG pipelines, the work highlights the practical value of principled explanations for neural ranking models and debugging IR systems. The authors emphasize integration with Pyserini and ir_datasets (with PyTerrier integration forthcoming) to lower barriers to entry and foster rigorous ExIR research and application.

Abstract

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.

ir_explain: a Python Library of Explainable IR Methods

TL;DR

The paper introduces ir_explain, an open-source Python library that unifies major Explainable IR (ExIR) methods across post-hoc (pointwise, pairwise, listwise) and interpretable-by-design paradigms, with integrated support for common IR toolkits and datasets. It outlines a modular architecture comprising three explainer families, probing, evaluation, visualization, and utilities to reproduce state-of-the-art baselines and experiment with new approaches. Through demonstrations on robustness, ranking-approximation, replicability, and RAG pipelines, the work highlights the practical value of principled explanations for neural ranking models and debugging IR systems. The authors emphasize integration with Pyserini and ir_datasets (with PyTerrier integration forthcoming) to lower barriers to entry and foster rigorous ExIR research and application.

Abstract

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.
Paper Structure (18 sections, 7 figures, 4 tables)

This paper contains 18 sections, 7 figures, 4 tables.

Table of Contents

  1. Introduction and motivation
  2. Related work
  3. IR Explain: background and design
  4. Pointwise Explanations
  5. Pairwise Explanations
  6. Listwise Explanations
  7. Interpretable by Design
  8. Probing
  9. Evaluation
  10. Visualization
  11. Utilities
  12. Demonstration of Use Cases
  13. Robustness of Pointwise Explanation
  14. Approximating a Ranking as Explanation?
  15. Usecase of Pairwise Explanation
  16. ...and 3 more sections

Figures (7)

  • Figure 1: The overall structure of ir_explain showing how the various components of the library are intended to be used in practice.
  • Figure 2: The inheritance class diagram of the different explanation modules used in ir_explain for explaining ranking decisions.
  • Figure 3: Example of a pointwise explainer LIRME with a query, document, and a set of parameters passed to it.
  • Figure 4: Example use-case of a pairwise explanation module to explain a query-document pair. A list of axiom objects can be passed to the explain function.
  • Figure 5: Example of a listwise explainer Multiplex with a query, ranked list, and a set of parameters passed to the explainer.
  • ...and 2 more figures