In this paper, we introduce a novel class of backdoors in transformer models, that, in contrast to prior art, are unelicitable in nature.

In Sec. 2 we include a We include a datasheet for our dataset following the methodology from "Datasheets for Datasets" Ge-17 In this section, we include the prompts from Gebru et al. [2021] in blue, and in For what purpose was the dataset created? Was there a specific task in mind? The dataset was created to facilitate research development on cloud removal in satellite imagery. Specifically, our task is more temporally aligned than previous benchmarks. 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?

Clouds in satellite imagery pose a significant challenge for downstream applications. A major challenge in current cloud removal research is the absence of a comprehensive benchmark and a sufficiently large and diverse training dataset.

For both learning rules, we prove overfitting is tempered.

Full prompts are available in Appendix A.1.

Details and more visualizations are in Appendices C and D.

The CROP benchmark includes 5,045 multiple-choice questions covering three difficulty levels.

Identifying causal effects is a key problem of interest across many disciplines. The two long-standing approaches to estimate causal effects are observational and experimental (randomized) studies.