@inproceedings{43a78aba15ad4150875b6adf0c1e5b64,
title = "Listwise Explanations for Ranking Models Using Multiple Explainers",
abstract = "This paper proposes a novel approach towards better interpretability of a trained text-based ranking model in a post-hoc manner. A popular approach for post-hoc interpretability text ranking models are based on locally approximating the model behavior using a simple ranker. Since rankings have multiple relevance factors and are aggregations of predictions, existing approaches that use a single ranker might not be sufficient to approximate a complex model, resulting in low fidelity. In this paper, we overcome this problem by considering multiple simple rankers to better approximate the entire ranking list from a black-box ranking model. We pose the problem of local approximation as a Generalized Preference Coverage (GPC) problem that incorporates multiple simple rankers towards the listwise explanation of ranking models. Our method Multiplex uses a linear programming approach to judiciously extract the explanation terms, so that to explain the entire ranking list. We conduct extensive experiments on a variety of ranking models and report fidelity improvements of 37%–54% over existing competitors. We finally compare explanations in terms of multiple relevance factors and topic aspects to better understand the logic of ranking decisions, showcasing our explainers{\textquoteright} practical utility.",
keywords = "Explanation, List-wise, Neural, Post-hoc, Ranking",
author = "Lijun Lyu and Avishek Anand",
note = "Green Open Access added to TU Delft Institutional Repository {\textquoteleft}You share, we take care!{\textquoteright} – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; 45th European Conference on Information Retrieval, ECIR 2023 ; Conference date: 02-04-2023 Through 06-04-2023",
year = "2023",
doi = "10.1007/978-3-031-28244-7_41",
language = "English",
isbn = "978-3-031-28243-0",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "653--668",
editor = "Jaap Kamps and Lorraine Goeuriot and Fabio Crestani and Maria Maistro and Hideo Joho and Brian Davis and Cathal Gurrin and Annalina Caputo and Udo Kruschwitz",
booktitle = "Advances in Information Retrieval - 45th European Conference on Information Retrieval, ECIR 2023, Proceedings",
}