Evaluating RAG Metrics in Applied Contexts: An Experiment, Its Findings and Its Limitations
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Computer Science > Computation and Language
Title:Evaluating RAG Metrics in Applied Contexts: An Experiment, Its Findings and Its Limitations
Abstract:This paper reports an empirical study evaluating the relevance of several RAG metrics. The experiment is based on a question-answering dataset created by human annotators from business data. The generated responses and retrieved spans of a RAG system are scored using evaluation metrics from four libraries (Ragas, DeepEval, RAGChecker, Opik). These metrics are compared to scores given by two evaluators, as well as to standard metrics such as recall. An analysis of correlations is conducted. Finally, we highlight certain limitations of our methodology, compare it to those used in the literature, and suggest some avenues for future research. This paper is an English translation of a paper originally published in the French-speaking workshop EvalLLM (Brabant, 2026).
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2607.07302 [cs.CL] |
| (or arXiv:2607.07302v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2607.07302
arXiv-issued DOI via DataCite (pending registration)
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