The article thoroughly investigates the MS MARCO Web Search dataset, focusing on its multilingual nature and data characteristics. It discusses the dataset's significant skew, particularly in query language distribution, which can potentially introduce bias in model performance. The analysis emphasizes the need for robust techniques to minimize test-train overlap for effective evaluation. The findings underline the importance of data-centric strategies in optimizing training datasets to address these imbalances and improve evaluation outcomes in web-scale information retrieval systems.
The MS MARCO Web Search dataset presents a multilingual landscape, uncovering significant data skew that may impact model performance and necessitates data-centric optimization techniques for improvement.
Through rigorous analysis, we found that while MS MARCO's diverse languages represent a broad spectrum, the skewed distribution of query languages emphasizes the need for balancing training data.
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