标题： 更少的LLM，更多的文档：寻找改进的RAG 显示英文标题

标题： Less LLM, More Documents: Searching for Improved RAG

摘要： 检索增强生成（RAG）将文档检索与大型语言模型（LLMs）结合在一起。 虽然扩大生成器可以提高准确性，但也增加了成本并限制了可部署性。 我们探索了一个正交维度：扩大检索器的语料库以减少对大型LLM的依赖。 实验结果表明，语料库扩展可以持续增强RAG，并且通常可以作为增加模型规模的替代方案，尽管在更大规模时收益会递减。 小型和中型生成器与更大的语料库经常能够匹敌具有较小语料库的大型模型；中型模型往往获得最大的收益，而小型和大型模型受益较少。 我们的分析表明，改进主要来自于答案段落覆盖范围的增加，而利用效率基本保持不变。 这些发现确立了一个有原则的语料库-生成器权衡：投资更大的语料库为更强的RAG提供了一条有效的路径，通常与扩大LLM本身相当。 显示英文摘要

摘要： Retrieval-Augmented Generation (RAG) couples document retrieval with large language models (LLMs). While scaling generators improves accuracy, it also raises cost and limits deployability. We explore an orthogonal axis: enlarging the retriever's corpus to reduce reliance on large LLMs. Experimental results show that corpus scaling consistently strengthens RAG and can often serve as a substitute for increasing model size, though with diminishing returns at larger scales. Small- and mid-sized generators paired with larger corpora often rival much larger models with smaller corpora; mid-sized models tend to gain the most, while tiny and large models benefit less. Our analysis shows that improvements arise primarily from increased coverage of answer-bearing passages, while utilization efficiency remains largely unchanged. These findings establish a principled corpus-generator trade-off: investing in larger corpora offers an effective path to stronger RAG, often comparable to enlarging the LLM itself.