As models grow more complex and societal demands for transparency increase with emerging regulations, explainability has become an increasingly important research area. However, despite its recognized relevance, progress in explainability research in information retrieval (IR) has been slower than in related fields. This full day workshop aims to advance research in explainable IR by providing a more in-depth platform to reflect on recent developments and facilitate discussions across both new and persistent challenges. Building upon the first edition of the workshop, which was a great success in bringing together multiple perspectives on explainability in IR, this second edition will focus on synthesizing a common agenda for the research community. Through a set of interactive activities, the workshop will bring together a diverse group of researchers to build a shared understanding of key tasks and challenges, and to help shape future directions for explainable IR research. The workshop will have as concrete outcomes a roadmap document and a special issue proposal for a journal issue on explainability in IR.

Mechanistic interpretation has greatly contributed to a more detailed understanding of generative language models, enabling significant progress in identifying structures that implement key behaviors through interactions between internal components.In contrast, interpretability in information retrieval (IR) remains relatively coarse-grained, and much is still unknown as to how IR models determine whether a document is relevant to a query.In this work, we address this gap by mechanistically analyzing how one commonly used model, a cross-encoder, estimates relevance.We find that the model extracts traditional relevance signals, such as term frequency and inverse document frequency, in early-to-middle layers.These concepts are then combined in later layers, similar to the well-known probabilistic ranking function, BM25.Overall, our analysis offers a more nuanced understanding of how IR models compute relevance.Isolating these components lays the groundwork for future interventions that could enhance transparency, mitigate safety risks, and improve scalability.

As models grow more complex and societal demands for transparency increase with emerging regulations, explainability has become an even more important research area. However, despite its recognized relevance, explainability research in IR has seen slower progress than in related fields. This full day workshop aims to advance research in explainable information retrieval by providing a more in-depth platform to reflect on recent developments and facilitate discussions to address new and persistent challenges. Our goal is to bring together a diverse group of researchers to build a shared understanding of key tasks and challenges that will lay the foundation for the future of explainable IR research.

Mechanistic interpretability research, which aims to uncover the internal processes of machine learning models, has gained significant attention. One state-of-the-art technique, activation patching, has been applied to analyzing neural ranker behavior in relation to information retrieval (IR) axioms. To date, however, this remains a rapidly evolving topic in IR, with no established methodology for measuring results or constructing datasets to ensure pronounced, robust, and consistent patching effects. In this study, based on experimental results, we provide recommendations on measuring patching effects and designing diagnostic datasets for investigating term frequency. We identify the rareness and informativeness of injected terms as a key factor influencing the magnitude of patching effects. Additionally, we find that low score differences between baseline and perturbed documents introduce significant noise, which can be mitigated by filtering or applying penalty scores to the metric. More generally, we provide practical recommendations for the reliable application of activation patching in IR, advancing future interpretability research of neural ranking models. Our code is available at https://github.com/polgrisha/best-practices-ir-patching.

As information retrieval (IR) systems, such as search engines and conversational agents, become ubiquitous in various domains, the need for transparent and explainable systems grows to ensure accountability, fairness, and unbiased results. Despite recent advances in explainable AI and IR techniques, there is no consensus on the definition of explainability. Existing approaches often treat it as a singular notion, disregarding the multidimensional definition postulated in the literature. In this paper, we use psychometrics and crowdsourcing to identify human-centered factors of explainability in Web search systems and introduce SSE (Search System Explainability), an evaluation metric for explainable IR (XIR) search systems. In a crowdsourced user study, we demonstrate SSE's ability to distinguish between explainable and non-explainable systems, showing that systems with higher scores indeed indicate greater interpretability. We hope that aside from these concrete contributions to XIR, this line of work will serve as a blueprint for similar explainability evaluation efforts in other domains of machine learning and natural language processing.

Neural models have demonstrated remarkable performance across diverse ranking tasks. However, the processes and internal mechanisms along which they determine relevance are still largely unknown. Existing approaches for analyzing neural ranker behavior with respect to IR properties rely either on assessing overall model behavior or employing probing methods that may offer an incomplete understanding of causal mechanisms. To provide a more granular understanding of internal model decision-making processes, we propose the use of causal interventions to reverse engineer neural rankers, and demonstrate how mechanistic interpretability methods can be used to isolate components satisfying term-frequency axioms within a ranking model. We identify a group of attention heads that detect duplicate tokens in earlier layers of the model, then communicate with downstream heads to compute overall document relevance. More generally, we propose that this style of mechanistic analysis opens up avenues for reverse engineering the processes neural retrieval models use to compute relevance. This work aims to initiate granular interpretability efforts that will not only benefit retrieval model development and training, but ultimately ensure safer deployment of these models.

Representations from large language models (LLMs) are known to be dominated by a small subset of dimensions with exceedingly high variance.Previous works have argued that although ablating these outlier dimensions in LLM representations hurts downstream performance, outlier dimensions are detrimental to the representational quality of embeddings.In this study, we investigate how fine-tuning impacts outlier dimensions and show that 1) outlier dimensions that occur in pre-training persist in fine-tuned models and 2) a single outlier dimension can complete downstream tasks with a minimal error rate.Our results suggest that outlier dimensions can encode crucial taskspecific knowledge and that the value of a representation in a single outlier dimension drives downstream model decisions.1

As information retrieval (IR) systems, such as search engines and conversational agents, become ubiquitous in various domains, the need for transparent and explainable systems grows to ensure accountability, fairness, and unbiased results. Despite many recent advances toward explainable AI and IR techniques, there is no consensus on what it means for a system to be explainable. Although a growing body of literature suggests that explainability is comprised of multiple subfactors [2, 5, 6], virtually all existing approaches treat it as a singular notion. Additionally, while neural retrieval models (NRMs) have become popular for their ability to achieve high performance[3, 4, 7, 8], research on the explainability of NRMs has been largely unexplored until recent years. Numerous questions remain unanswered regarding the most effective means of comprehending how these intricate models arrive at their decisions and the extent to which these methods will function efficiently for both developers and end-users.