Retrieval-augmented generation (RAG) is a promising method for addressing some of the memory-related challenges associated with Large Language Models (LLMs). Two separate systems form the RAG pipeline, the retriever and the reader, and the impact of each on downstream task performance is not well-understood. Here, we work towards the goal of understanding how retrievers can be optimized for RAG pipelines for common tasks such as Question Answering (QA). We conduct experiments focused on the relationship between retrieval and RAG performance on QA and attributed QA and unveil a number of insights useful to practitioners developing high-performance RAG pipelines. For example, lowering search accuracy has minor implications for RAG performance while potentially increasing retrieval speed and memory efficiency.

翻译：检索增强生成（RAG）是解决大语言模型（LLM）相关记忆挑战的一种前景广阔的方法。RAG流程由检索器与阅读器两个独立系统构成，而各自对下游任务性能的影响尚未得到充分理解。本文致力于探究如何针对问答（QA）等常见任务，为RAG流程优化检索器。我们通过实验聚焦于检索与RAG在QA及归因QA任务上的性能关联，揭示了若干对开发高性能RAG系统具有实践价值的发现。例如，适度降低搜索精度对RAG性能影响甚微，却能显著提升检索速度与内存效率。