A Counterfactual Explanation Framework for Retrieval Models

[2409.00860] A Counterfactual Explanation Framework for Retrieval Models Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Information Retrieval arXiv:2409.00860 (cs) [Submitted on 1 Sep 2024 ( v1 ), last revised 14 Apr 2026 (this version, v4)] Title: A Counterfactual Explanation Framework for Retrieval Models Authors: Bhavik Chandna , Procheta Sen View a PDF of the paper titled A Counterfactual Explanation Framework for Retrieval Models, by Bhavik Chandna and 1 other authors View PDF HTML (experimental) Abstract: Explainability has become a crucial concern in today's world, aiming to enhance transparency in machine learning and deep learning models. Information retrieval is no exception to this trend. In existing literature on explainability of information retrieval, the emphasis has predominantly been on illustrating the concept of relevance concerning a retrieval model. The questions addressed include why a document is relevant to a query, why one document exhibits higher relevance than another, or why a specific set of documents is deemed relevant for a query. However, limited attention has been given to understanding why a particular document is not favored (e.g., not within top-K) with respect to a query and a retrieval model. In an effort to address this gap, our work focuses on the question of what terms need to be added within a document to improve its ranking. This, in turn, answers the question of which words in the document played a role in not being favored by a retrieval model for a particular query. We use a counterfactual framework to solve the above-mentioned research problem. % To the best of our knowledge, we mark the first attempt to tackle this specific counterfactual problem (i.e. examining the absence of which words can affect the ranking of a document). Our experiments show the effectiveness of our proposed approach in predicting counterfactuals for both statistical (e.g. BM25) and deep-learning-based models (e.g. DRMM, DSSM, ColBERT, MonoT5). Subjects: Information Retrieval (cs.IR) Cite as: arXiv:2409.00860 [cs.IR] (or arXiv:2409.00860v4 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2409.00860 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Procheta Sen [ view email ] [v1] Sun, 1 Sep 2024 22:33:29 UTC (882 KB) [v2] Tue, 10 Sep 2024 10:52:30 UTC (883 KB) [v3] Sat, 8 Mar 2025 21:02:16 UTC (1,167 KB) [v4] Tue, 14 Apr 2026 11:36:34 UTC (1,274 KB) Full-text links: Access Paper: View a PDF of the paper titled A Counterfactual Explanation Framework for Retrieval Models, by Bhavik Chandna and 1 other authors View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.IR < prev | next > new | recent | 2024-09 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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