Recent studies have shown promising results on utilizing large pre-trained image-language models for video question answering.

Pre-trained large text-to-image models synthesize impressive images with an appropriate use of text prompts.

Deep learning practice is increasingly driven by powerful foundation models (FM), pre-trained at scale and then fine-tuned for specific tasks of interest.

The concept of "incidental correlations" is derived from the notion of incidental endogeneity [

In this work, we investigate how an adversary can exploit instruction tuning by injecting specific instruction-following examples into the training data that intentionally changes the model's behavior.

However, recent works reveal that it is non-trivial to find a proper initialization of the prompt tokens.