Recent advances in large language models have demonstrated remarkable
effectiveness in information retrieval (IR) tasks. While many neural IR systems
encode queries and documents into single-vector representations, multi-vector
models elevate the retrieval quality by producing multi-vector representations
and facilitating similarity searches at the granularity of individual tokens.
However, these models significantly amplify memory and storage requirements for
retrieval indices by an order of magnitude. This escalation in index size
renders the scalability of multi-vector IR models progressively challenging due
to their substantial memory demands. We introduce Embedding from Storage
Pipelined Network (ESPN) where we offload the entire re-ranking embedding
tables to SSDs and reduce the memory requirements by 5-16x. We design a
software prefetcher with hit rates exceeding 90%, improving SSD based retrieval
up to 6.4x, and demonstrate that we can maintain near memory levels of query
latency even for large query batch sizes.

最近大规模语言模型在信息检索任务中显示出了显著的有效性。然而，多向量模型在检索索引的存储和内存方面需要更大的需求，这使得多向量 IR 模型的可扩展性逐渐面临挑战。本文介绍了一种名为 ESPN 的存储引入的嵌入式网络模型，通过将整个重排序嵌入表存储在 SSD 中并减少内存需求 5-16 倍。我们设计了一个软件预取器，其命中率超过 90%，从而将基于 SSD 的检索性能提高了最高达 6.4 倍，并且在大查询批处理的情况下能够保持接近内存级别的查询延迟。

ESPN：高效内存多向量信息检索

ESPN: Memory-Efficient Multi-Vector Information Retrieval

End-to-end question answering (QA) requires both information retrieval (IR)
over a large document collection and machine reading comprehension (MRC) on the
retrieved passages. Recent work has successfully trained neural IR systems
using only supervised question answering (QA) examples from open-domain
datasets. However, despite impressive performance on Wikipedia, neural IR lags
behind traditional term matching approaches such as BM25 in more specific and
specialized target domains such as COVID-19. Furthermore, given little or no
labeled data, effective adaptation of QA systems can also be challenging in
such target domains. In this work, we explore the application of synthetically
generated QA examples to improve performance on closed-domain retrieval and
MRC. We combine our neural IR and MRC systems and show significant improvements
in end-to-end QA on the CORD-19 collection over a state-of-the-art open-domain
QA baseline.

本文探讨了如何通过合成的问答数据来提高封闭领域检索和机器阅读理解的性能，实验结果表明将神经信息检索系统和机器阅读理解系统组合后，在 CORD-19 数据集上相比于基准开放领域问答系统，有显著的改进。