Efficient Inverted Indexes for Approximate Retrieval over Learned Sparse Representations

TL;DR

This paper introduces Seismic, a novel inverted index organization that enables fast, approximate retrieval over learned sparse embeddings, significantly improving speed while maintaining high recall compared to existing solutions.

Contribution

The paper proposes a new index structure with geometrically-cohesive blocks and summary vectors, achieving rapid approximate retrieval over learned sparse representations.

Findings

Seismic achieves sub-millisecond per-query latency.

It outperforms state-of-the-art inverted index solutions by 10-100 times.

It surpasses the BigANN Challenge winners in speed and recall.

Abstract

Learned sparse representations form an attractive class of contextual embeddings for text retrieval. That is so because they are effective models of relevance and are interpretable by design. Despite their apparent compatibility with inverted indexes, however, retrieval over sparse embeddings remains challenging. That is due to the distributional differences between learned embeddings and term frequency-based lexical models of relevance such as BM25. Recognizing this challenge, a great deal of research has gone into, among other things, designing retrieval algorithms tailored to the properties of learned sparse representations, including approximate retrieval systems. In fact, this task featured prominently in the latest BigANN Challenge at NeurIPS 2023, where approximate algorithms were evaluated on a large benchmark dataset by throughput and recall. In this work, we propose a novel organization of the inverted index that enables fast yet effective approximate retrieval over learned sparse embeddings. Our approach organizes inverted lists into geometrically-cohesive blocks, each equipped with a summary vector. During query processing, we quickly determine if a block must be evaluated using the summaries. As we show experimentally, single-threaded query processing using our method, Seismic, reaches sub-millisecond per-query latency on various sparse embeddings of the MS MARCO dataset while maintaining high recall. Our results indicate that Seismic is one to two orders of magnitude faster than state-of-the-art inverted index-based solutions and further outperforms the winning (graph-based) submissions to the BigANN Challenge by a significant margin.