# Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey

arXiv: https://arxiv.org/abs/2504.14891 · HTML: https://mlsanity.com/paper/2504.14891/

## Key facts

- Cited by 20 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 101 of its 124 references that are in the map.
- First posted on arXiv 21 Apr 2025.

## Placed in

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

## Builds on (most cited first, 30 of 101)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://mlsanity.com/paper/2310.11511/) (2023)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [Gorilla: Large Language Model Connected with Massive APIs](https://mlsanity.com/paper/2305.15334/) (2023)
- [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://mlsanity.com/paper/2305.14251/) (2023)
- [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://mlsanity.com/paper/2212.03533/) (2022)
- [Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions](https://mlsanity.com/paper/2212.10509/) (2022)
- [Graph of Thoughts: Solving Elaborate Problems with Large Language Models](https://mlsanity.com/paper/2308.09687/) (2023)
- [MTEB: Massive Text Embedding Benchmark](https://mlsanity.com/paper/2210.07316/) (2022)
- [Instruction Tuning for Large Language Models: A Survey](https://mlsanity.com/paper/2308.10792/) (2023)
- [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://mlsanity.com/paper/2102.07350/) (2021)
- [Wizard of Wikipedia: Knowledge-Powered Conversational agents](https://mlsanity.com/paper/1811.01241/) (2018)
- [ERASER: A Benchmark to Evaluate Rationalized NLP Models](https://mlsanity.com/paper/1911.03429/) (2019)
- [A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2405.06211/) (2024)
- [Ragas: Automated Evaluation of Retrieval Augmented Generation](https://mlsanity.com/paper/2309.15217/) (2023)
- [The Faiss library](https://mlsanity.com/paper/2401.08281/) (2024)
- [GPTScore: Evaluate as You Desire](https://mlsanity.com/paper/2302.04166/) (2023)
- [Retrieval-Augmented Generation for AI-Generated Content: A Survey](https://mlsanity.com/paper/2402.19473/) (2024)
- [Benchmarking Retrieval-Augmented Generation for Medicine](https://mlsanity.com/paper/2402.13178/) (2024)
- [FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation](https://mlsanity.com/paper/2310.03214/) (2023)
- [Benchmarking Large Language Models in Retrieval-Augmented Generation](https://mlsanity.com/paper/2309.01431/) (2023)
- [PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models](https://mlsanity.com/paper/2402.07867/) (2024)
- [ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2311.09476/) (2023)

## Built on by (most cited first, 20 of 20)

- [Get Experience from Practice: LLM Agents with Record & Replay](https://mlsanity.com/paper/2505.17716/) (2025)
- [Unlocking Multimodal Document Intelligence: From Current Triumphs to Future Frontiers of Visual Document Retrieval](https://mlsanity.com/paper/2602.19961/) (2026)
- [OmniRAG-Agent: Agentic Omnimodal Reasoning for Low-Resource Long Audio-Video Question Answering](https://mlsanity.com/paper/2602.03707/) (2026)
- [LLM-Centric RAG with Multi-Granular Indexing and Confidence Constraints](https://mlsanity.com/paper/2510.27054/) (2025)
- [A Principle-based Framework for the Development and Evaluation of Large Language Models for Health and Wellness](https://mlsanity.com/paper/2512.08936/) (2025)
- [Securing Retrieval-Augmented Generation: A Taxonomy of Attacks, Defenses, and Future Directions](https://mlsanity.com/paper/2604.08304/) (2026)
- [VikingMem: A Memory Base Management System for Stateful LLM-based Applications](https://mlsanity.com/paper/2605.29640/) (2026)
- [Testing Retrieval-Augmented Generation Systems with Chunk Coverage](https://mlsanity.com/paper/2607.18155/) (2026)
- [When Knowledge Changes: Metamorphic Testing of RAG Systems with Mutations](https://mlsanity.com/paper/2607.26843/) (2026)
- [InterpDetect: Interpretable Signals for Detecting Hallucinations in Retrieval-Augmented Generation](https://mlsanity.com/paper/2510.21538/) (2025)
- [Empirical Evaluation of PDF Parsing and Chunking for Financial Question Answering with RAG](https://mlsanity.com/paper/2604.12047/) (2026)
- [Retrieval-Augmented Generation Must Move Beyond Factual Grounding to Represent Diverse Opinions](https://mlsanity.com/paper/2604.12138/) (2026)
- [Towards Query-Agnostic RAG Evaluation via Query Coverage and Claim Verifiability](https://mlsanity.com/paper/2608.11238/) (2026)
- [RAGVUE: A Diagnostic View for Explainable and Automated Evaluation of Retrieval-Augmented Generation](https://mlsanity.com/paper/2601.04196/) (2025)
- [M'as contexto no es mejor. Paradoja de la diluci'on vectorial en RAG corporativos](https://mlsanity.com/paper/2601.08851/) (2025)
- [Guaranteeing Knowledge Integration with Joint Decoding for Retrieval-Augmented Generation](https://mlsanity.com/paper/2604.08046/) (2026)
- [Adaptive Rigor in AI System Evaluation using Temperature-Controlled Verdict Aggregation via Generalized Power Mean](https://mlsanity.com/paper/2604.08595/) (2026)
- [Reducing Hallucinations in Complex Question Answering using Simple Graph-based Retrieval-Augmented Generation (long version)](https://mlsanity.com/paper/2606.05901/) (2026)
- [Retrieval Augmented Generation Framework for the Nepali Legal Domain Question Answering](https://mlsanity.com/paper/2606.07523/) (2026)
- [Retrieve, Reproduce, Reveal: Dissecting Retrieval-Augmented Software Vulnerability Detection](https://mlsanity.com/paper/2609.37669/) (2026)

## Abstract (arXiv)

Recent advancements in Retrieval-Augmented Generation (RAG) have revolutionized natural language processing by integrating Large Language Models (LLMs) with external information retrieval, enabling accurate, up-to-date, and verifiable text generation across diverse applications. However, evaluating RAG systems presents unique challenges due to their hybrid architecture that combines retrieval and generation components, as well as their dependence on dynamic knowledge sources in the LLM era. In response, this paper provides a comprehensive survey of RAG evaluation methods and frameworks, systematically reviewing traditional and emerging evaluation approaches, for system performance, factual accuracy, safety, and computational efficiency in the LLM era. We also compile and categorize the RAG-specific datasets and evaluation frameworks, conducting a meta-analysis of evaluation practices in high-impact RAG research. To the best of our knowledge, this work represents the most comprehensive survey for RAG evaluation, bridging traditional and LLM-driven methods, and serves as a critical resource for advancing RAG development.