RAGe: A Retrieval-Augmented Generation Evaluation Framework

• URL: http://arxiv.org/abs/2605.27445v1
• Date: Sat, 23 May 2026 17:46:47 GMT
• Title: RAGe: A Retrieval-Augmented Generation Evaluation Framework
• Abstract summary: RAGe supports researchers to identify the most effective, domain-specific RAG setups for their specific operational needs.<n> RAGe supports researchers to identify the most effective, domain-specific RAG setups for their specific operational needs, facilitating rapid prototyping even on consumer-grade hardware.
• Score: 3.833693802518811
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Deploying Large Language Model (LLM) applications, particularly those relying on Retrieval-Augmented Generation (RAG), remains challenging due to high computational demands, outdated knowledge bases, and the need to manually select optimal pipeline components. In this work, we propose a modular framework for benchmarking and guiding the efficient development of RAG applications by focusing on resource telemetry and component recommendation, suggesting the best components for a domain-specific dataset. Our approach leverages core techniques in LLM applications, including document chunking, vector databases, embedding models, and retrievers, to evaluate trade-offs among accuracy, efficiency, and scalability. By directly correlating retrieval and generation quality with underlying hardware constraints, RAGe supports researchers to identify the most effective, domain-specific RAG setups for their specific operational needs, facilitating rapid prototyping even on consumer-grade hardware.

Related papers

• Structure-Aware RAG: Structured Retrieval Augmented Generation from Noisy Data for Conversational Agents [68.52563127206808]
  Structure-aware Retrieval Augmented Generation (SA-RAG) uses tables as an intermediate structured representation to provide a compact and controllable interface.<n>We introduce a quality-aware table metadata generation framework that models metadata normalization and effectiveness.<n>Experiments on two noisy real-world datasets show that SA-RAG significantly outperforms existing RAG baselines.
  arXiv  Detail & Related papers  (2026-05-23T03:07:33Z)
• Relatron: Automating Relational Machine Learning over Relational Databases [50.94254514286021]
  We present a study that unifies RDL and DFS in a shared design space and conducts architecture-centric searches across diverse RDB tasks.<n>Our analysis yields three key findings: (1) RDL does not consistently outperform DFS, with performance being highly task-dependent; (2) no single architecture dominates across tasks, underscoring the need for task-aware model selection; and accuracy is an unreliable guide for choice architecture.
  arXiv  Detail & Related papers  (2026-02-26T02:45:22Z)
• RAGalyst: Automated Human-Aligned Agentic Evaluation for Domain-Specific RAG [0.0]
  Retrieval-Augmented Generation (RAG) is a critical technique for grounding Large Language Models (LLMs) in factual evidence.<n>Existing evaluation frameworks often rely on metrics that fail to capture domain-specific nuances.<n>This paper introduces RAGalyst, an automated, human-aligned agentic framework designed for the rigorous evaluation of domain-specific RAG systems.
  arXiv  Detail & Related papers  (2025-11-06T16:22:52Z)
• Domain-Specific Data Generation Framework for RAG Adaptation [58.20906914537952]
  Retrieval-Augmented Generation (RAG) combines the language understanding and reasoning power of large language models with external retrieval to enable domain-grounded responses.<n>We propose RAGen, a framework for generating domain-grounded question-answer-context (QAC) triples tailored to diverse RAG adaptation approaches.
  arXiv  Detail & Related papers  (2025-10-13T09:59:49Z)
• mmRAG: A Modular Benchmark for Retrieval-Augmented Generation over Text, Tables, and Knowledge Graphs [11.861763118322136]
  We introduce mmRAG, a modular benchmark for evaluating multi-modal RAG systems.<n>Our benchmark integrates queries from six diverse question-answering datasets spanning text, tables, and knowledge graphs.<n>We follow standard information retrieval procedures to annotate document relevance and derive dataset relevance.
  arXiv  Detail & Related papers  (2025-05-16T12:31:29Z)
• RoseRAG: Robust Retrieval-augmented Generation with Small-scale LLMs via Margin-aware Preference Optimization [53.63439735067081]
  Large language models (LLMs) have achieved impressive performance but face high computational costs and latency.<n>Retrieval-augmented generation (RAG) helps by integrating external knowledge, but imperfect retrieval can introduce distracting noise that misleads SLMs.<n>We propose RoseRAG, a robust RAG framework for SLMs via Margin-aware Preference Optimization.
  arXiv  Detail & Related papers  (2025-02-16T04:56:53Z)
• Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement Learning [88.55095746156428]
  Retrieval-augmented generation (RAG) is widely utilized to incorporate external knowledge into large language models.<n>A standard RAG pipeline consists of several components, such as query rewriting, document retrieval, document filtering, and answer generation.<n>We propose treating the complex RAG pipeline with multiple components as a multi-agent cooperative task, in which each component can be regarded as an RL agent.
  arXiv  Detail & Related papers  (2025-01-25T14:24:50Z)
• XRAG: eXamining the Core -- Benchmarking Foundational Components in Advanced Retrieval-Augmented Generation [36.84847781022757]
  Retrieval-augmented generation (RAG) synergizes the retrieval of pertinent data with the generative capabilities of Large Language Models (LLMs)<n>We introduce XRAG, an open-source, modular that facilitates exhaustive evaluation of the performance of foundational components of advanced RAG modules.
  arXiv  Detail & Related papers  (2024-12-20T03:37:07Z)
• Query Optimization for Parametric Knowledge Refinement in Retrieval-Augmented Large Language Models [25.390142515721802]
  The ERRR framework is designed to bridge the pre-retrieval information gap in Retrieval-Augmented Generation (RAG) systems.<n>Unlike conventional query optimization techniques used in RAG, the ERRR framework begins by extracting knowledge from Large Language Models (LLMs)
  arXiv  Detail & Related papers  (2024-11-12T14:12:45Z)
• FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research [70.6584488911715]
  retrieval-augmented generation (RAG) has attracted considerable research attention.<n>Existing RAG toolkits are often heavy and inflexibly, failing to meet the customization needs of researchers.<n>Our toolkit has implemented 16 advanced RAG methods and gathered and organized 38 benchmark datasets.
  arXiv  Detail & Related papers  (2024-05-22T12:12:40Z)
• CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models [49.16989035566899]
  Retrieval-Augmented Generation (RAG) is a technique that enhances the capabilities of large language models (LLMs) by incorporating external knowledge sources. This paper constructs a large-scale and more comprehensive benchmark, and evaluates all the components of RAG systems in various RAG application scenarios.
  arXiv  Detail & Related papers  (2024-01-30T14:25:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.