This paper demonstrates RUBEN, an interactive tool for discovering minimal rules to explain the outputs of retrieval-augmented large language models (LLMs) in data-driven applications. We leverage novel pruning strategies to efficiently identify a minimal set of rules that subsume all others. We further demonstrate novel applications of these rules for LLM safety, specifically to test the resiliency of safety training and effectiveness of adversarial prompt injections.

This paper demonstrates RAGE, an interactive tool for explaining Large Language Models (LLMs) augmented with retrieval capabilities; i.e., able to query external sources and pull relevant information into their input context. Our explanations are counterfactual in the sense that they identify parts of the input context that, when removed, change the answer to the question posed to the LLM. RAGE includes pruning methods to navigate the vast space of possible explanations, allowing users to view the provenance of the produced answers.

In an era where artificial intelligence (AI) is re-shaping countless aspects of society, we present a forward-looking perspective for enhancing the explainability of large language models (LLMs), with a particular focus on the retrieval-augmented generation (RAG) prompting technique. We motivate the urgency for developing techniques to explain LLM decision-making behaviour, especially as these models are deployed in critical sectors. Central to this effort is RAGE, our novel explain-ability tool that can trace the provenance of an LLM's answer back to external knowledge sources provided via RAG. RAGE builds upon established explainability techniques to recover citations for LLM answers, identify context biases, and mine answer rules. Through our novel explainability formulations and practical use cases, we chart a course toward more transparent and trustworthy AI technologies.

Towards better explainability in the field of information retrieval, we present CREDENCE, an interactive tool capable of generating counterfactual explanations for document rankers. Embracing the unique properties of the ranking problem, we present counterfactual explanations in terms of document perturbations, query perturbations, and even other documents. Additionally, users may build and test their own perturbations, and extract insights about their query, documents, and ranker.