RUBEN: Rule-Based Explanations for Retrieval-Augmented LLM Systems
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.
Fields of Study & Subjects
Topic Modeling
Explainable Artificial Intelligence (XAI)
Multimodal Machine Learning Applications
Publication details
- Journal / source
- 2026 IEEE 42nd International Conference on Data Engineering (ICDE)
- Publisher
- IEEE
- Pages
- 4198–4202
- Publication date
- 1 May 2026