Retrieval-augmented generation (RAG) systems combine document retrieval with a generative model to address complex information seeking tasks like report generation. While the relationship between retrieval quality and generation effectiveness seems intuitive, it has not been systematically studied. We investigate whether upstream retrieval metrics can serve as reliable early indicators of the final generated response's information coverage. Through experiments across two text RAG benchmarks (TREC NeuCLIR 2024 and TREC RAG 2024) and one multimodal benchmark (WikiVideo), we analyze 15 text retrieval stacks and 10 multimodal retrieval stacks across four RAG pipelines and multiple evaluation frameworks (Auto-ARGUE and MiRAGE). Our findings demonstrate strong correlations between coverage-based retrieval metrics and nugget coverage in generated responses at both topic and system levels. This relationship holds most strongly when retrieval objectives align with generation goals, though more complex iterative RAG pipelines can partially decouple generation quality from retrieval effectiveness. These findings provide empirical support for using retrieval metrics as proxies for RAG performance.

翻译：检索增强生成（RAG）系统将文档检索与生成模型相结合，以应对报告生成等复杂的信息寻求任务。尽管检索质量与生成效果之间的关系看似直观，但尚未得到系统研究。我们探究上游检索指标能否作为最终生成响应信息覆盖范围的可靠早期指标。通过在两个文本RAG基准（TREC NeuCLIR 2024和TREC RAG 2024）和一个多模态基准（WikiVideo）上的实验，我们分析了四个RAG流水线和多个评估框架（Auto-ARGUE和MiRAGE）中的15个文本检索栈和10个多模态检索栈。我们的研究结果表明，基于覆盖率的检索指标与生成响应中的信息单元覆盖率在主题层面和系统层面均存在强相关性。当检索目标与生成目标一致时，这种关系最为显著，尽管更复杂的迭代式RAG流水线能够部分解耦生成质量与检索效果。这些发现为使用检索指标作为RAG性能的代理提供了实证支持。