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In this work we describe our submission to the product ranking task of theAmazon KDD Cup 2022. We rely on a receipt that showed to be effective inprevious competitions: we focus our efforts towards efficiently training anddeploying large language odels, such as mT5, while reducing to a minimum thenumber of task-specific adaptations. Despite the simplicity of our approach,our best model was less than 0.004 nDCG@20 below the top submission. As the top20 teams achieved an nDCG@20 close to .90, we argue that we need more difficulte-Commerce evaluation datasets to discriminate retrieval methods.

A Boring-yet-effective Approach for the Product Ranking Task of the Amazon KDD Cup 2022