Title: A Dense Subset Index for Collective Query Coverage
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: collective retrieval, subset search, submodular functions

Abstract: In traditional information retrieval, corpus items compete with each other to occupy top ranks in response to a query.  In contrast, in many recent retrieval scenarios associated with complex, multi-hop question answering or text-to-SQL, items are not self-complete: they must instead collaborate, i.e., information from multiple items must be combined to respond to the query. In the context of modern dense retrieval, this need translates into finding a small collection of corpus items whose contextual word vectors collectively cover the contextual word vectors of the query. The central challenge is to retrieve a near-optimal collection of covering items in time that is sublinear in corpus size. By establishing coverage as a submodular objective, we enable successive dense index probes to quickly assemble an item collection that achieves near-optimal coverage.  Successive query vectors are iteratively `edited', and the dense index is built using random projections of a novel, lifted dense vector space. Beyond rigorous theoretical guarantees, we report on a scalable implementation of this new form of vector database. Extensive experiments establish the empirical success of DISCo, in terms of the best coverage vs. query latency tradeoffs.

TL;DR: DISCo casts retrieval as submodular coverage to enable scalable and collaborative subset search.