Neural information retrieval (IR) has transitioned from using classical human-defined relevance rules to leveraging complex neural models for retrieval tasks. While benefiting from advances in machine learning (ML), neural IR also inherits several drawbacks, including the opacity of the model’s decision-making process. This thesis aims to tackle this issue and enhance the transparency of neural IR models. Particularly, our work focuses on understanding which input features neural ranking models rely on to generate a specific ranking list. Our work draws inspiration from interpretable ML. However, we also recognize the unique aspects of IR tasks, which guide our development of methods specifically designed to interpret IR models....

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.