RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework

• URL: http://arxiv.org/abs/2408.01262v4
• Date: Thu, 17 Oct 2024 02:20:47 GMT
• Title: RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework
• Authors: Kunlun Zhu, Yifan Luo, Dingling Xu, Ruobing Wang, Shi Yu, Shuo Wang, Yukun Yan, Zhenghao Liu, Xu Han, Zhiyuan Liu, Maosong Sun,
• Abstract summary: 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.
• Score: 69.4501863547618
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Retrieval-Augmented Generation (RAG) is a powerful approach that enables large language models (LLMs) to incorporate external knowledge. However, evaluating the effectiveness of RAG systems in specialized scenarios remains challenging due to the high costs of data construction and the lack of suitable evaluation metrics. This paper introduces RAGEval, a framework designed to assess RAG systems across diverse scenarios by generating high-quality documents, questions, answers, and references through a schema-based pipeline. With a focus on factual accuracy, we propose three novel metrics Completeness, Hallucination, and Irrelevance to rigorously evaluate LLM-generated responses. Experimental results show that RAGEval outperforms zero-shot and one-shot methods in terms of clarity, safety, conformity, and richness of generated samples. Furthermore, the use of LLMs for scoring the proposed metrics demonstrates a high level of consistency with human evaluations. RAGEval establishes a new paradigm for evaluating RAG systems in real-world applications.

Related papers

• OmniEval: An Omnidirectional and Automatic RAG Evaluation Benchmark in Financial Domain [62.89809156574998]
  We introduce an omnidirectional and automatic RAG benchmark, OmniEval, in the financial domain. Our benchmark is characterized by its multi-dimensional evaluation framework. Our experiments demonstrate the comprehensiveness of OmniEval, which includes extensive test datasets.
  arXiv  Detail & Related papers  (2024-12-17T15:38:42Z)
• Unanswerability Evaluation for Retrieval Augmented Generation [74.3022365715597]
  UAEval4RAG is a framework designed to evaluate whether RAG systems can handle unanswerable queries effectively. We define a taxonomy with six unanswerable categories, and UAEval4RAG automatically synthesizes diverse and challenging queries.
  arXiv  Detail & Related papers  (2024-12-16T19:11:55Z)
• 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)
• Controlling Risk of Retrieval-augmented Generation: A Counterfactual Prompting Framework [77.45983464131977]
  We focus on how likely it is that a RAG model's prediction is incorrect, resulting in uncontrollable risks in real-world applications. Our research identifies two critical latent factors affecting RAG's confidence in its predictions. We develop a counterfactual prompting framework that induces the models to alter these factors and analyzes the effect on their answers.
  arXiv  Detail & Related papers  (2024-09-24T14:52:14Z)
• 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)
• 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)
• 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)
• RAGAS: Automated Evaluation of Retrieval Augmented Generation [25.402461447140823]
  RAGAs is a framework for evaluation of Retrieval Augmented Generation pipelines. RAG systems are composed of a retrieval and an LLM based generation module.
  arXiv  Detail & Related papers  (2023-09-26T19:23:54Z)

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.