Bridging the Gap Between Indexing and Retrieval for Differentiable Search Index with Query Generation

TL;DR

This paper introduces DSI-QG, a new framework that improves differentiable search indexes by generating relevant queries during indexing, thereby reducing data mismatch issues and enhancing retrieval performance across languages.

Contribution

The paper proposes DSI-QG, a novel indexing method that uses query generation and re-ranking to align training and retrieval data distributions in differentiable search indexes.

Findings

DSI-QG significantly outperforms original DSI models on multiple datasets.

Query generation during indexing improves cross-lingual retrieval accuracy.

Mitigating data distribution mismatch enhances overall retrieval effectiveness.

Abstract

The Differentiable Search Index (DSI) is an emerging paradigm for information retrieval. Unlike traditional retrieval architectures where index and retrieval are two different and separate components, DSI uses a single transformer model to perform both indexing and retrieval. In this paper, we identify and tackle an important issue of current DSI models: the data distribution mismatch that occurs between the DSI indexing and retrieval processes. Specifically, we argue that, at indexing, current DSI methods learn to build connections between the text of long documents and the identifier of the documents, but then retrieval of document identifiers is based on queries that are commonly much shorter than the indexed documents. This problem is further exacerbated when using DSI for cross-lingual retrieval, where document text and query text are in different languages. To address this fundamental problem of current DSI models, we propose a simple yet effective indexing framework for DSI, called DSI-QG. When indexing, DSI-QG represents documents with a number of potentially relevant queries generated by a query generation model and re-ranked and filtered by a cross-encoder ranker. The presence of these queries at indexing allows the DSI models to connect a document identifier to a set of queries, hence mitigating data distribution mismatches present between the indexing and the retrieval phases. Empirical results on popular mono-lingual and cross-lingual passage retrieval datasets show that DSI-QG significantly outperforms the original DSI model.