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Shortcut Learning of Large Language Models in Natural Language Understanding: A Survey Megan Du1, Fencing He2, Na Zou1, Aachen Tao2, and Xia Hu3 1 Texas A&M University 2 JD Explore Academy 3 Rice

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What is shortcut learning of large?

Shortcut learning of large refers to the phenomenon where a machine learning model quickly adapts to specific features in large datasets, often at the expense of generalization.

Who is required to file shortcut learning of large?

Entities such as researchers, data scientists, and organizations that deploy machine learning models using large datasets are required to file shortcut learning of large.

How to fill out shortcut learning of large?

To fill out shortcut learning of large, one must provide information concerning the dataset, model architecture, findings from the learning process, and any biases or limitations.

What is the purpose of shortcut learning of large?

The purpose of shortcut learning of large is to improve model performance by leveraging specific patterns in data, while being cautious about overfitting.

What information must be reported on shortcut learning of large?

Information that must be reported includes dataset characteristics, model parameters, performance metrics, and the observed behaviors of the model.