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The linked article is about the importance of structured outputs in machine learning models. It explains how structured outputs, such as sequences, graphs, and tables, can capture complex relationships in data and improve the performance of models compared to traditional classification or regression tasks. The article discusses the challenges and benefits of working with structured outputs and provides examples of use cases where they can be effectively applied, such as language generation, drug discovery, and recommender systems.