A Methodology for Evaluating RAG Systems: A Case Study On Configuration Dependency Validation
- URL: http://arxiv.org/abs/2410.08801v1
- Date: Fri, 11 Oct 2024 13:36:13 GMT
- Title: A Methodology for Evaluating RAG Systems: A Case Study On Configuration Dependency Validation
- Authors: Sebastian Simon, Alina Mailach, Johannes Dorn, Norbert Siegmund,
- Abstract summary: Retrieval-augmented generation (RAG) is an umbrella of different components, design decisions, and domain-specific adaptations.
There is currently no generally accepted methodology for RAG evaluation despite a growing interest in this technology.
We propose a first blueprint of a methodology for a sound and reliable evaluation of RAG systems.
- Score: 6.544757635738911
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Retrieval-augmented generation (RAG) is an umbrella of different components, design decisions, and domain-specific adaptations to enhance the capabilities of large language models and counter their limitations regarding hallucination and outdated and missing knowledge. Since it is unclear which design decisions lead to a satisfactory performance, developing RAG systems is often experimental and needs to follow a systematic and sound methodology to gain sound and reliable results. However, there is currently no generally accepted methodology for RAG evaluation despite a growing interest in this technology. In this paper, we propose a first blueprint of a methodology for a sound and reliable evaluation of RAG systems and demonstrate its applicability on a real-world software engineering research task: the validation of configuration dependencies across software technologies. In summary, we make two novel contributions: (i) A novel, reusable methodological design for evaluating RAG systems, including a demonstration that represents a guideline, and (ii) a RAG system, which has been developed following this methodology, that achieves the highest accuracy in the field of dependency validation. For the blueprint's demonstration, the key insights are the crucial role of choosing appropriate baselines and metrics, the necessity for systematic RAG refinements derived from qualitative failure analysis, as well as the reporting practices of key design decision to foster replication and evaluation.
Related papers
- CoFE-RAG: A Comprehensive Full-chain Evaluation Framework for Retrieval-Augmented Generation with Enhanced Data Diversity [23.48167670445722]
Retrieval-Augmented Generation (RAG) aims to generate more accurate and reliable answers with the help of the retrieved context from external knowledge sources.
evaluating these systems remains a crucial research area due to the following issues.
We propose a Comprehensive Full-chain Evaluation (CoFE-RAG) framework to facilitate thorough evaluation across the entire RAG pipeline.
arXiv Detail & Related papers (2024-10-16T05:20:32Z) - Trustworthiness in Retrieval-Augmented Generation Systems: A Survey [59.26328612791924]
Retrieval-Augmented Generation (RAG) has quickly grown into a pivotal paradigm in the development of Large Language Models (LLMs)
We propose a unified framework that assesses the trustworthiness of RAG systems across six key dimensions: factuality, robustness, fairness, transparency, accountability, and privacy.
arXiv Detail & Related papers (2024-09-16T09:06:44Z) - Boosting CNN-based Handwriting Recognition Systems with Learnable Relaxation Labeling [48.78361527873024]
We propose a novel approach to handwriting recognition that integrates the strengths of two distinct methodologies.
We introduce a sparsification technique that accelerates the convergence of the algorithm and enhances the overall system's performance.
arXiv Detail & Related papers (2024-09-09T15:12:28Z) - VERA: Validation and Evaluation of Retrieval-Augmented Systems [5.709401805125129]
VERA is a framework designed to enhance the transparency and reliability of outputs from large language models (LLMs)
We show how VERA can strengthen decision-making processes and trust in AI applications.
arXiv Detail & Related papers (2024-08-16T21:59:59Z) - RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation [61.14660526363607]
We propose a fine-grained evaluation framework, RAGChecker, that incorporates a suite of diagnostic metrics for both the retrieval and generation modules.
RAGChecker has significantly better correlations with human judgments than other evaluation metrics.
The metrics of RAGChecker can guide researchers and practitioners in developing more effective RAG systems.
arXiv Detail & Related papers (2024-08-15T10:20:54Z) - RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework [69.4501863547618]
This paper introduces RAGEval, a framework designed to assess RAG systems across diverse scenarios.
With a focus on factual accuracy, we propose three novel metrics Completeness, Hallucination, and Irrelevance.
Experimental results show that RAGEval outperforms zero-shot and one-shot methods in terms of clarity, safety, conformity, and richness of generated samples.
arXiv Detail & Related papers (2024-08-02T13:35:11Z) - A Study on the Implementation Method of an Agent-Based Advanced RAG System Using Graph [0.0]
This study implements an advanced RAG system based on Graph technology to develop high-quality generative AI services.
It employs LangGraph to evaluate the reliability of retrieved information and synthesizes diverse data to generate more accurate and enhanced responses.
arXiv Detail & Related papers (2024-07-29T13:26:43Z) - Evaluation of Retrieval-Augmented Generation: A Survey [13.633909177683462]
We provide a comprehensive overview of the evaluation and benchmarks of Retrieval-Augmented Generation (RAG) systems.
Specifically, we examine and compare several quantifiable metrics of the Retrieval and Generation components, such as relevance, accuracy, and faithfulness.
We then analyze the various datasets and metrics, discuss the limitations of current benchmarks, and suggest potential directions to advance the field of RAG benchmarks.
arXiv Detail & Related papers (2024-05-13T02:33:25Z) - REX: Rapid Exploration and eXploitation for AI Agents [103.68453326880456]
We propose an enhanced approach for Rapid Exploration and eXploitation for AI Agents called REX.
REX introduces an additional layer of rewards and integrates concepts similar to Upper Confidence Bound (UCB) scores, leading to more robust and efficient AI agent performance.
arXiv Detail & Related papers (2023-07-18T04:26:33Z) - A Domain-Agnostic Approach for Characterization of Lifelong Learning
Systems [128.63953314853327]
"Lifelong Learning" systems are capable of 1) Continuous Learning, 2) Transfer and Adaptation, and 3) Scalability.
We show that this suite of metrics can inform the development of varied and complex Lifelong Learning systems.
arXiv Detail & Related papers (2023-01-18T21:58:54Z)
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.