mlsanityData to 14 Oct 2026

RAGE Against the Machine: Retrieval-Augmented LLM Explanations

arXiv 2405.13000 ↗First posted 11 May 2024cs.CL cs.AI cs.IRcited by 8

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

Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 4 of its 7 references that are in the map. First posted on arXiv 11 May 2024.

Builds on

4 of 7 references.

References · 4

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  2. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
  3. 2023Larger language models do in-context learning differentlycited by 394
  4. 2023CREDENCE: Counterfactual Explanations for Document Rankingcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 4; 2026: 2.

8 papers.

  1. 2025Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Surveycited by 30
  2. 2024GraphInsight: Unlocking Insights in Large Language Models for Graph Structure Understandingcited by 18
  3. 2025KGRAG-Ex: Explainable Retrieval-Augmented Generation with Knowledge Graph-based Perturbationscited by 4
  4. 2024MAPLE: Enhancing Review Generation with Multi-Aspect Prompt LEarning in Explainable Recommendationcited by 3
  5. 2026RUBEN: Rule-Based Explanations for Retrieval-Augmented LLM Systemscited by 1
  6. 2025Rule-Based Explanations for Retrieval-Augmented LLM Systemscited by 0
  7. 2025TempPerturb-Eval: On the Joint Effects of Internal Temperature and External Perturbations in RAG Robustnesscited by 0
  8. 2026XGRAG: A Graph-Native Framework for Explaining KG-based Retrieval-Augmented Generationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Explain-Query-Test: Self-Evaluating LLMs Via Explanation and Comprehension Discrepancy
  2. 2024Large Language Model Can Be a Foundation for Hidden Rationale-Based Retrieval
  3. 2024Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely
  4. 2023RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit
  5. 2026Rational Synthesizers or Heuristic Followers? Analyzing LLMs in RAG-based Question-Answering
  6. 2025Post-training an LLM for RAG? Train on Self-Generated Demonstrations
  7. 2025When Retrieval Succeeds and Fails: Rethinking Retrieval-Augmented Generation for LLMs
  8. 2024Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG