减少大语言模型，增加文档：探索更优的检索增强生成

摘要

检索增强生成（RAG）将文档检索与大规模语言模型（LLMs）相结合。虽然扩展生成器能提升准确性，但也增加了成本并限制了可部署性。我们探索了一个正交方向：扩大检索器的语料库以减少对大型LLMs的依赖。实验结果表明，语料库的扩展持续强化了RAG，并常可作为增大模型规模的替代方案，尽管在更大规模时收益递减。中小型生成器搭配更大语料库，往往能与使用较小语料库的更大模型相媲美；中型模型通常获益最多，而微型和大型模型受益较少。我们的分析显示，改进主要源于答案相关段落覆盖率的提升，而利用效率基本保持不变。这些发现确立了一个原则性的语料库-生成器权衡：投资于更大的语料库为增强RAG提供了一条有效途径，其效果常可与扩大LLM本身相提并论。

English

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.

减少大语言模型，增加文档：探索更优的检索增强生成

Less LLM, More Documents: Searching for Improved RAG

摘要

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