Evaluation of Large Language Models Enhanced with Retrieval-Augmented Generation: A literature review
Abstract
Large Language Models (LLMs) demonstrate remarkable capabilities in text generation but face persistent challenges with hallucination and factual accuracy. Retrieval-Augmented Generation (RAG) addresses these limitations by combining generative models with external knowledge retrieval. However, standardized evaluation frameworks remain lacking. This literature review analyzes eleven evaluation frameworks for LLMs and RAG systems published between 2023 and 2025, examining their methodologies, metrics, and approaches to hallucination detection. The frameworks span diverse domains and employ varying automation levels, from manual annotation to LLM-as-a-Judge paradigms.Our analysis reveals a paradigm shift toward automated and hybrid evaluation approaches. Despite progress, challenges persist in reproducibility, scalability, and cross-domain generalization. This review synthesizes best practices and identifies directions for developing robust, standardized evaluation methodologies essential for trustworthy RAG system deployment.
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