内存有效的开域QA索引内容修剪
Pruning the Index Contents for Memory Efficient Open-Domain QA
摘要：
这项工作提出了一个新的管道，展示了什么是可以实现的国家的最先进的方法的综合努力。具体来说，它提出了一种新的R2-D2（两次排名，两次读取）管道，包括检索器，通道reranker，提取阅读器，生成阅读器和一个简单的方法来组合它们。此外，以前的工作通常带有大量外部文档的索引，其规模为数十GiB。这项工作提出了一种简单的方法来修剪大规模索引的内容，使得开放域QA系统连同索引、操作系统和库组件一起适合6 GiB Docker映像，同时仅保留8%的原始索引内容，仅损失3%的EM准确性。
英文摘要：
This work presents a novel pipeline that demonstrates what is achievable with a combined effort of state-of-the-art approaches. Specifically, it proposes the novel R2-D2 (Rank twice, reaD twice) pipeline composed of retriever, passage reranker, extractive reader, generative reader and a simple way to combine them. Furthermore, previous work often comes with a massive index of external documents that scales in the order of tens of GiB. This work presents a simple approach for pruning the contents of a massive index such that the open-domain QA system altogether with index, OS, and library components fits into 6GiB docker image while retaining only 8% of original index contents and losing only 3% EM accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
