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RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation

A fine-grained evaluation framework for Retrieval-Augmented Generation (RAG) systems.

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RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation

By Dongyu Ru, Lin Qiu, Xiangkun Hu, Tianhang Zhang, Peng Shi, Shuaichen Chang, Jiayang, Cheng, Cunxiang WangarXiv (Cornell University)
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The paper proposes RAGChecker, a framework that incorporates diagnostic metrics for evaluating RAG systems. It includes a suite of metrics for both retrieval and generation modules, which are verified to have better correlations with human judgments than other evaluation metrics.

The authors use RAGChecker to evaluate eight RAG systems and analyze their performance.

Abstract

The paper proposes RAGChecker, a framework that incorporates diagnostic metrics for evaluating RAG systems. It includes a suite of metrics for both retrieval and generation modules, which are verified to have better correlations with human judgments than other evaluation metrics. The authors use RAGChecker to evaluate eight RAG systems and analyze their performance.

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evaluation frameworkretrieval-augmented generationdiagnostic metricsfine-grained evaluationragsystemsLarge Language ModelsRetrieval & RAGSemantic InteroperabilityContent Engineering
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