mlsanityData to 7 Oct 2026

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

arXiv 2605.10862 ↗First posted 11 May 2026cs.CLcited by 1

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 32 of its 61 references that are in the map. First posted on arXiv 11 May 2026.

Builds on

32 of 61 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024RAGE Against the Machine: Retrieval-Augmented LLM Explanations11 May

Other references · 31

  1. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  2. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  3. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  4. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  5. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,086
  6. 2023Can Large Language Models Be an Alternative to Human Evaluations?cited by 729
  7. 2023Towards Automated Circuit Discovery for Mechanistic Interpretabilitycited by 700
  8. 2019Explaining Machine Learning Classifiers through Diverse Counterfactual Explanationscited by 612
  9. 2023Enabling Large Language Models to Generate Text with Citationscited by 584
  10. 2019Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methodscited by 440
  11. 2020Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Reviewcited by 174
  12. 2023Do Language Models Know When They're Hallucinating References?cited by 115
19 more
  1. 2024ContextCite: Attributing Model Generation to Contextcited by 93
  2. 2025Poisoning Attacks on LLMs Require a Near-constant Number of Poison Samplescited by 66
  3. 2019Avoiding Reasoning Shortcuts: Adversarial Evaluation, Training, and Model Development for Multi-Hop QAcited by 63
  4. 2019Understanding Dataset Design Choices for Multi-hop Reasoningcited by 50
  5. 2024CiteME: Can Language Models Accurately Cite Scientific Claims?cited by 45
  6. 2020Decisions, Counterfactual Explanations and Strategic Behaviorcited by 44
  7. 2024LLM-Ensemble: Optimal Large Language Model Ensemble Method for E-commerce Product Attribute Value Extractioncited by 39
  8. 2020Citation Recommendation: Approaches and Datasetscited by 36
  9. 2024Source-Aware Training Enables Knowledge Attribution in Language Modelscited by 29
  10. 2024Citation-Enhanced Generation for LLM-based Chatbotscited by 24
  11. 2024InterpBench: Semi-Synthetic Transformers for Evaluating Mechanistic Interpretability Techniquescited by 24
  12. 2025Hard Negatives, Hard Lessons: Revisiting Training Data Quality for Robust Information Retrieval with LLMscited by 17
  13. 2024Verifiable Generation with Subsentence-Level Fine-Grained Citationscited by 15
  14. 2024Learning to Generate Answers with Citations via Factual Consistency Modelscited by 13
  15. 2022BASED-XAI: Breaking Ablation Studies Down for Explainable Artificial Intelligencecited by 12
  16. 2021Mitigating False-Negative Contexts in Multi-document Question Answering with Retrieval Marginalizationcited by 10
  17. 2025CiteFix: Enhancing RAG Accuracy Through Post-Processing Citation Correctioncited by 8
  18. 2015The Retrieval Effectiveness of Web Search Engines: Considering Results Descriptionscited by 4
  19. 2022Computing Rule-Based Explanations by Leveraging Counterfactualscited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026ExecRubrics: Executable Tool-Augmented Rubrics for Verifiable and Efficient Long-Form Evaluationcited by 0

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  1. 2025Rule-Based Explanations for Retrieval-Augmented LLM Systems
  2. 2025Learned-Rule-Augmented Large Language Model Evaluators
  3. 2024Let your LLM generate a few tokens and you will reduce the need for retrieval
  4. 2025Retrieval Augmented Question Answering: When Should LLMs Admit Ignorance?
  5. 2023Query Rewriting for Retrieval-Augmented Large Language Models
  6. 2024Feedback-Guided Extraction of Knowledge Base from Retrieval-Augmented LLM Applications
  7. 2026AdversarialCoT: Single-Document Retrieval Poisoning for LLM Reasoning
  8. 2025Do Retrieval-Augmented Language Models Adapt to Varying User Needs?