SLIM: Sparsified Late Interaction for Multi-Vector Retrieval with Inverted Indexes

This paper introduces a method called Sparsified Late Interaction for Multi-vector retrieval with inverted indexes (SLIM). Although multi-vector models have demonstrated their effectiveness in various information retrieval tasks, most of their pipelines require custom optimization to be efficient in both time and space. Among them, ColBERT is probably the most established method which is based on the late interaction of contextualized token embeddings of pre-trained language models. Unlike ColBERT where all its token embeddings are low-dimensional and dense, SLIM projects each token embedding into a high-dimensional, sparse lexical space before performing late interaction. In practice, we further propose to approximate SLIM using the lower- and upper-bound of the late interaction to reduce latency and storage. In this way, the sparse outputs can be easily incorporated into an inverted search index and are fully compatible with off-the-shelf search tools such as Pyserini and Elasticsearch. SLIM has competitive accuracy on information retrieval benchmarks such as MS MARCO Passages and BEIR compared to ColBERT while being much smaller and faster on CPUs. Source code and data will be available at https://github.com/castorini/pyserini/blob/master/docs/experiments-slim.md.