Question: Discuss the two main conditions under which a machine learning practitioner should consider down-weighting auxiliary training data, and explain how this practice impacts the computational resources required for the model.

Sample answer: A practitioner should consider down-weighting auxiliary data when the additional data has a significantly different distribution from the dev/test set, or when it is vastly larger than the target-distribution data. Down-weighting is beneficial because it reduces the overall computational burden. Specifically, by assigning lower weight to large amounts of auxiliary data, the model does not need to be as massive or complex to perform well on both the auxiliary and target tasks, thereby saving significant computational resources.

Key points:

• Auxiliary data has a very different distribution than the dev/test set.
• Auxiliary data is much larger than target-distribution data.
• Down-weighting reduces the computational burden of the training process.
• It prevents the necessity of building an overly massive neural network.

Rubric: A strong response will correctly identify the two conditions for down-weighting (different distribution and disproportionately large size) and clearly explain that it reduces the need for a massive neural network, thereby saving computational resources.