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@@ -431,7 +431,7 @@ serves as its relevance label. We employ various downstream task metrics to obtain document-level annotations and aggregate them using set-based or ranking metrics. Extensive experiments on a wide range of datasets demonstrate that eRAG achieves a higher -correlation with downstream RAG performance compared to baseline methods, with improvements in Kendall’s 𝜏 correlation ranging +correlation with downstream RAG performance compared to baseline methods, with improvements in Kendall’s tau correlation ranging from 0.168 to 0.494. Additionally, eRAG offers significant computational advantages, improving runtime and consuming up to 50 times less GPU memory than end-to-end evaluation. |