Abstract
In this work we describe our submission to the product ranking task of the Amazon KDD Cup 2022. We rely on a receipt that showed to be effective in previous competitions: we focus our efforts towards efficiently training and deploying large language odels, such as mT5, while reducing to a minimum the number 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 top 20 teams achieved an nDCG@20 close to .90, we argue that we need more difficult e-Commerce evaluation datasets to discriminate retrieval methods.
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