# RAGE Against the Machine: Retrieval-Augmented LLM Explanations

arXiv: https://arxiv.org/abs/2405.13000 · HTML: https://mlsanity.com/paper/2405.13000/

## 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.

## Placed in

- Retrieval-augmented generation / [RAG pipeline design and evaluation](https://mlsanity.com/rag/rag-pipeline-design-and-evaluation/)

## Builds on (most cited first, 4 of 4)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Larger language models do in-context learning differently](https://mlsanity.com/paper/2303.03846/) (2023)
- [CREDENCE: Counterfactual Explanations for Document Ranking](https://mlsanity.com/paper/2302.04983/) (2023)

## Built on by (most cited first, 8 of 8)

- [Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2502.06872/) (2025)
- [GraphInsight: Unlocking Insights in Large Language Models for Graph Structure Understanding](https://mlsanity.com/paper/2409.03258/) (2024)
- [KGRAG-Ex: Explainable Retrieval-Augmented Generation with Knowledge Graph-based Perturbations](https://mlsanity.com/paper/2507.08443/) (2025)
- [MAPLE: Enhancing Review Generation with Multi-Aspect Prompt LEarning in Explainable Recommendation](https://mlsanity.com/paper/2408.09865/) (2024)
- [RUBEN: Rule-Based Explanations for Retrieval-Augmented LLM Systems](https://mlsanity.com/paper/2605.10862/) (2026)
- [Rule-Based Explanations for Retrieval-Augmented LLM Systems](https://mlsanity.com/paper/2510.22689/) (2025)
- [TempPerturb-Eval: On the Joint Effects of Internal Temperature and External Perturbations in RAG Robustness](https://mlsanity.com/paper/2512.01183/) (2025)
- [XGRAG: A Graph-Native Framework for Explaining KG-based Retrieval-Augmented Generation](https://mlsanity.com/paper/2604.24623/) (2026)

## 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.