RUBEN: Rule-Based Explanations for Retrieval-Augmented LLM Systems

Joel Rorseth; Parke Godfrey; Lukasz Golab; Divesh Srivastava; Jarek Szlichta

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arXiv · 2605.10862

RUBEN: Rule-Based Explanations for Retrieval-Augmented LLM Systems

Joel Rorseth·Parke Godfrey·Lukasz Golab·Divesh Srivastava·Jarek Szlichta

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Abstract

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.

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Joel Rorseth, Parke Godfrey, Lukasz Golab, Divesh Srivastava, Jarek Szlichta. 2026-05-11. RUBEN: Rule-Based Explanations for Retrieval-Augmented LLM Systems. https://arxiv.org/abs/2605.10862

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