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MS-Shift: an analysis of MS MARCO distribution shifts on neural retrieval
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| Simon Lupart, Thibault Formal, Stéphane Clinchant |
| 45th European Conference on Information Retrieval (ECIR), Dublin, Ireland, 2–6 April, 2023 |
| arXiv |
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 mono BERT reranker. We further analyse the performance drops between the train and test query distributions. In particular, we identify two generalization indicators: the first one based on train/test query vocabulary overlap, the second based on each model retrieval score. 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.
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The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
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