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A.1 Motivation For what purpose was the dataset created? Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., Who funded the creation of the dataset? What do the instances that comprise the dataset represent (e.g., documents, photos, people, How many instances are there in total (of each type, if appropriate)? The SRFUND dataset contains all possible instances. What data does each instance consist of?

Removing such large groups can result in significant changes to the model.

We thank R1 for their comments and will emphasize the broader implications of our work on model explainability. R2 asked to contrast using (i) influence functions to measure the importance of training points with (ii) existing These papers address a different problem setting from ours and their methods are correspondingly distinct. Despite their differences, these methods could be complementary, as R2 suggested. We will include this discussion and we thank R2 for pointing it out. R3 asked if our empirical findings hold for non-convex models.

Explaining model behavior has emerged as a central goal within ML.

This dataset encompasses human preferences in text-to-video generation tasks along two primary dimensions: helpfulness and harmlessness.

Below we describe the steps for calculating metrics. To avoid overfitting on our dataset, we used already fitted image-and video-quality-assessment models with public source code. Below are the steps for calculating different versions of such metrics. We used mean temporal pooling as a way to aggregate scores from multiple frames. We intend to include more data on this research in future publications.

A.1 Motivation For what purpose was the dataset created? Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., Who funded the creation of the dataset? What do the instances that comprise the dataset represent (e.g., documents, photos, people, How many instances are there in total (of each type, if appropriate)? The SRFUND dataset contains all possible instances. What data does each instance consist of?

This dataset encompasses human preferences in text-to-video generation tasks along two primary dimensions: helpfulness and harmlessness.