Abstract

Recent years have seen enormous gains in core information retrieval tasks, including document and passage ranking. Datasets and leaderboards, and in particular the MS MARCO datasets, illustrate the dramatic improvements achieved by modern neural rankers. When compared with traditional information retrieval test collections, such as those developed by TREC, the MS MARCO datasets employ substantially more queries — thousands vs. dozens – with substantially fewer known relevant items per query — often just one. For example, 94% of the nearly seven thousand queries in the MS MARCO passage ranking development set have only a single known relevant passage, and no query has more than four. Given the sparsity of these relevance labels, the MS MARCO leaderboards track improvements with mean reciprocal rank (MRR). In essence, the known relevant item is treated as the “right answer” or “best answer”, with rankers scored on their ability to place this item as high in the ranking as possible. In working with these sparse labels, we have observed that the top items returned by a ranker often appear superior to judged relevant items. Others have reported the same observation.