Transformer Memory as a Differentiable Search Index

TL;DR

This paper introduces the Differentiable Search Index (DSI), a novel approach where a Transformer model encodes an entire corpus within its parameters to perform direct text-to-docid retrieval, simplifying and enhancing information retrieval.

Contribution

The paper presents DSI, a new paradigm that enables direct retrieval by training a Transformer to map queries to document IDs, outperforming traditional baselines and demonstrating strong generalization.

Findings

DSI outperforms dual encoder baselines.

DSI surpasses BM25 in zero-shot retrieval.

Model and corpus size variations affect performance.

Abstract

In this paper, we demonstrate that information retrieval can be accomplished with a single Transformer, in which all information about the corpus is encoded in the parameters of the model. To this end, we introduce the Differentiable Search Index (DSI), a new paradigm that learns a text-to-text model that maps string queries directly to relevant docids; in other words, a DSI model answers queries directly using only its parameters, dramatically simplifying the whole retrieval process. We study variations in how documents and their identifiers are represented, variations in training procedures, and the interplay between models and corpus sizes. Experiments demonstrate that given appropriate design choices, DSI significantly outperforms strong baselines such as dual encoder models. Moreover, DSI demonstrates strong generalization capabilities, outperforming a BM25 baseline in a zero-shot setup.