将聚类倒排索引与 kNN 图配对以实现学习稀疏表示上的快速近似检索
Pairing Clustered Inverted Indexes with kNN Graphs for Fast Approximate Retrieval over Learned Sparse Representations
AI总结：
本文提出 SeismicWave，通过按重要性顺序遍历倒排块并利用 kNN 图扩展候选文档，在保持几乎精确准确率的同时将 Seismic 的近似检索速度提升至 2.2 倍。
摘要：
学习稀疏表示构成了一类有效且可解释的文本检索嵌入。尽管在此类嵌入上进行精确 top-k 检索面临效率挑战，但最近一种名为 Seismic 的算法已实现了显著快速且高精度的近似检索。Seismic 静态剪枝倒排列表，将每个列表组织成几何内聚的块，并为每个块增加一个摘要向量。在查询时，与查询词关联的每个倒排列表以任意顺序逐块遍历，通过查询与摘要之间的内积判断是否必须评估某个块。当一个块被认为有希望时，其文档将通过前向索引进行完整评估。Seismic 比最先进的基于倒排索引的解决方案快一到两个数量级，并显著优于 BigANN 2023 Challenge 中获胜的基于图的提交方案。在这项工作中，我们通过对其查询处理子例程引入两项创新来进一步加速 Seismic。第一，我们按重要性顺序遍历块，而不是任意顺序。第二，我们取 Seismic 检索到的文档列表，并使用离线 k-正则最近邻图将其扩展以包含每个文档的邻居；随后对扩展后的列表进行排序以产生最终的 top-k 集合。在两个公开数据集上的实验表明，我们的扩展方法名为 SeismicWave，可以达到几乎精确的准确率水平，并且比 Seismic 快至多 2.2 倍。
英文摘要：
Learned sparse representations form an effective and interpretable class of embeddings for text retrieval. While exact top-k retrieval over such embeddings faces efficiency challenges, a recent algorithm called Seismic has enabled remarkably fast, highly-accurate approximate retrieval. Seismic statically prunes inverted lists, organizes each list into geometrically-cohesive blocks, and augments each block with a summary vector. At query time, each inverted list associated with a query term is traversed one block at a time in an arbitrary order, with the inner product between the query and summaries determining if a block must be evaluated. When a block is deemed promising, its documents are fully evaluated with a forward index. Seismic is one to two orders of magnitude faster than state-of-the-art inverted index-based solutions and significantly outperforms the winning graph-based submissions to the BigANN 2023 Challenge. In this work, we speed up Seismic further by introducing two innovations to its query processing subroutine. First, we traverse blocks in order of importance, rather than arbitrarily. Second, we take the list of documents retrieved by Seismic and expand it to include the neighbors of each document using an offline k-regular nearest neighbor graph; the expanded list is then ranked to produce the final top-k set. Experiments on two public datasets show that our extension, named SeismicWave, can reach almost-exact accuracy levels and is up to 2.2x faster than Seismic.
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。
