The article critiques existing information retrieval benchmarks, highlighting their limitations in utilizing web-scale data and addressing the diverse needs of multilingual queries. It particularly emphasizes the inadequacy of traditionally used datasets, which often contain too few labeled queries to effectively train deep learning models. While new datasets like MS MARCO and ORCAS have emerged to provide more extensive options for AI research, the article underscores that issues such as biases and data richness need greater attention to further enhance AI information retrieval capabilities.
Existing benchmarks for information retrieval often fail to leverage web-scale data, which hampers the ability of AI researchers to develop truly sophisticated models.
Diversity and volume in query datasets remain critical. Current resources like MS MARCO and ORCAS, while extensive, still exhibit biases, warranting further advancement.
Collection
[
|
...
]