Automated data curation is essential for managing the vast amounts of information and data processed by AI models. To mitigate issues such as data duplication and imbalance, efficient curation methods must be employed. Additionally, research has questioned the accuracy of claims regarding the performance of AI models like GPT-4 on standardized tests, revealing potential biases in reported achievements.

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