TY - GEN

T1 - Listwise Explanations for Ranking Models Using Multiple Explainers

AU - Lyu, Lijun

AU - Anand, Avishek

N1 - Funding Information: Acknowledgements. This work is partially supported by German Research Foundation (DFG), under the Project IREM with grant No. AN 996/1-1.

PY - 2023

Y1 - 2023

N2 - 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’ practical utility.

AB - 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’ practical utility.

KW - Explanation

KW - List-wise

KW - Neural

KW - Post-hoc

KW - Ranking

UR - http://www.scopus.com/inward/record.url?scp=85151134683&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-28244-7_41

DO - 10.1007/978-3-031-28244-7_41

M3 - Conference contribution

AN - SCOPUS:85151134683

SN - 9783031282430

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 653

EP - 668

BT - Advances in Information Retrieval - 45th European Conference on Information Retrieval, ECIR 2023, Proceedings

A2 - Kamps, Jaap

A2 - Goeuriot, Lorraine

A2 - Crestani, Fabio

A2 - Maistro, Maria

A2 - Joho, Hideo

A2 - Davis, Brian

A2 - Gurrin, Cathal

A2 - Caputo, Annalina

A2 - Kruschwitz, Udo

CY - Cham

T2 - 45th European Conference on Information Retrieval, ECIR 2023

Y2 - 2 April 2023 through 6 April 2023

ER -