MS-Shift: An Analysis of MS MARCO Distribution Shifts on Neural Retrieval

TL;DR

This paper investigates how neural retrieval models based on BERT perform under explicit distribution shifts within MS MARCO, revealing varying robustness among different approaches and providing resources for future benchmarking.

Contribution

It introduces three controlled distribution shifts in MS MARCO and analyzes their impact on various neural retrieval models, enhancing understanding of model generalization.

Findings

Dense models are most affected by distribution shifts.

Performance drops correlate with vocabulary and representation dissimilarity.

MS MARCO query subsets are released for benchmarking zero-shot transfer.

Abstract

Pre-trained Language Models have recently emerged in Information Retrieval as providing the backbone of a new generation of neural systems that outperform traditional methods on a variety of tasks. However, it is still unclear to what extent such approaches generalize in zero-shot conditions. The recent BEIR benchmark provides partial answers to this question by comparing models on datasets and tasks that differ from the training conditions. We aim to address the same question by comparing models under more explicit distribution shifts. To this end, we build three query-based distribution shifts within MS MARCO (query-semantic, query-intent, query-length), which are used to evaluate the three main families of neural retrievers based on BERT: sparse, dense, and late-interaction -- as well as a monoBERT re-ranker. We further analyse the performance drops between the train and test query distributions. In particular, we experiment with two generalization indicators: the first one based on train/test query vocabulary overlap, and the second based on representations of a trained bi-encoder. Intuitively, those indicators verify that the further away the test set is from the train one, the worse the drop in performance. We also show that models respond differently to the shifts -- dense approaches being the most impacted. Overall, our study demonstrates that it is possible to design more controllable distribution shifts as a tool to better understand generalization of IR models. Finally, we release the MS MARCO query subsets, which provide an additional resource to benchmark zero-shot transfer in Information Retrieval.