Pre-trained language models based on transformer networks are highly effective for document re-ranking in ad-hoc search. Among these, cross-encoders stand out for their effectiveness, as they process query-document pairs through the entire transformer network to compute ranking scores. However, this traversal is computationally expensive. To address this, prior work has explored early-exit strategies, enabling the model to terminate the traversal of query-document pairs. These techniques rely on learned classifiers, placed after each transformer block, that decide if a query-document pair can be dropped. Diverging from previous approaches, we propose Similarity-based Early Exit (SEE), a novel-non-learned-strategy that exploits the similarities between query and document token embeddings to early-terminate the inference of documents that will most likely be non-relevant to the query. Even though SEE can be used after every transformer block, we show that the best advantage is achieved when applied before the first transformer block, thus saving most of the inference cost for the query-document pairs. Reproducible experiments on 17 public datasets covering in-domain and out-of-domain evaluation show that SEE can be effectively applied to four different cross-encoders, achieving speedups of up to 3.5× with a limited loss in ranking effectiveness.

Learning ranking models that are both explainable and effective is an emerging topic within the research area of explainable AI. Several Learning-to-Rank (LtR) algorithms have been recently proposed that build models that are simple to explain and, at the same time, almost as effective as their state-of-the-art, black-box counterparts. In this work, we propose Interpretable LambdaMART (ILMART), a novel framework with different strategies to constrain the state-of-the-art LtR LambdaMART algorithm to generate interpretable models, i.e., ensembles whose trees can use either single features (main effects) or a limited number of interacting features (interaction effects). ILMART facilitates a straightforward tradeoff between model explainability and effectiveness by precisely tuning the quantity of main and interaction effects during the learning phase. We show that slightly increasing their number allows ILMART models to reach ranking performances at par with full-complexity LambdaMART ones. Furthermore, reproducible experiments conducted on publicly available LtR datasets demonstrate that ILMART can improve nDCG@10 by up to 10% compared to state-of-the-art competitors while preserving an explainable structure. Finally, we explore the relationship between model explainability and inference efficiency by introducing a novel and easy-to-implement scoring algorithm for ILMART ranking models, achieving up to a \(100\times\) speedup compared to the baseline.

Understanding the behavior of deep neural networks for Information Retrieval (IR) is crucial to improve trust in these effective models. Current popular approaches to diagnose the predictions made by deep neural networks are mainly based on: i) the adherence of the retrieval model to some axiomatic property of the IR system, ii) the generation of free-text explanations, or iii) feature importance attributions. In this work, we propose a novel approach that analyzes the changes of document and query embeddings in the latent space and that might explain the inner workings of IR large pre-trained language models. In particular, we focus on predicting query/document relevance, and we characterize the predictions by analyzing the topological arrangement of the embeddings in their latent space and their evolution while passing through the layers of the network. We show that there exists a link between the embedding adjustment and the predicted score, based on how tokens cluster in the embedding space. This novel approach, grounded in the query and document tokens interplay over the latent space, provides a new perspective on neural ranker explanation and a promising strategy for improving the efficiency of the models and Query Performance Prediction (QPP).

State-of-the-art methods for Learning to Rank (LtR), either designed for tabular or textual data, are incredibly complex. Increasing the complexity of the models has many drawbacks, including difficulties in understanding the logic behind each prediction and a lack of trust in the system during its deployment. In this paper, which describes the author's goals during his Ph.D., there is an analysis and discussion of how we can use the ideas and tools coming from the eXplainable Artificial Intelligence (XAI) field to make the most effective methods for LtR understandable to the practitioners with the final goal of making them more efficient and/or understand better when they can be improved. The strategies adopted to achieve the aforementioned goals are different and based on the type of models analyzed, which go from more traditional LtR models based on ensembles of decision trees and using handcrafted features to fairly new neural LtR models using text data.

Interpretable Learning to Rank (LtR) is an emerging field within the research area of explainable AI, aiming at developing intelligible and accurate predictive models. While most of the previous research efforts focus on creating post-hoc explanations, in this paper we investigate how to train effective and intrinsically-interpretable ranking models. Developing these models is particularly challenging and it also requires finding a trade-off between ranking quality and model complexity. State-of-the-art rankers, made of either large ensembles of trees or several neural layers, exploit in fact an unlimited number of feature interactions making them black boxes. Previous approaches on intrinsically-interpretable ranking models address this issue by avoiding interactions between features thus paying a significant performance drop with respect to full-complexity models. Conversely, ILMART, our novel and interpretable LtR solution based on LambdaMART, is able to train effective and intelligible models by exploiting a limited and controlled number of pairwise feature interactions. Exhaustive and reproducible experiments conducted on three publicly-available LtR datasets show that ILMART outperforms the current state-of-the-art solution for interpretable ranking of a large margin with a gain of nDCG of up to 8%.