论文标题
亚马逊KDD杯2022的产品排名任务的无聊效果方法
A Boring-yet-effective Approach for the Product Ranking Task of the Amazon KDD Cup 2022
论文作者
论文摘要
在这项工作中，我们描述了我们对2022年亚马逊KDD CUP的产品排名任务的提交。我们依靠收据表明在以前的比赛中有效：我们将精力集中在有效地培训和部署大型语言ODEL上，例如MT5，同时减少了最少的特定任务适应。尽管我们的方法很简单，但我们的最佳模型低于0.004 NDCG@20以下。随着前20个团队获得了接近.90的NDCG@20，我们认为我们需要更困难的电子商务评估数据集来区分检索方法。
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.
