Our main results are summarized below.

The deployment of ever-larger machine learning models reflects a growing consensus that the more expressive the model class one optimizes over--and the more data one has access to--the more one can improve performance. As models get deployed in a variety of real-world scenarios, they inevitably face strategic environments.

Table 3 presents comprehensive details of the TrojAI dataset. PICCOLO is a backdoor scanning tool aiming at detecting whether a language model is backdoored. It cannot reverse engineer exact triggers but optimizes a list of surrogate triggers that can induce ASR. The surrogate triggers by PICCOLO cannot be directly used. Table 4 documents the optimal prompts identified via fuzzing for each model.

Data coming from different sensors often capture information related to common latent factors.

Large-scale pre-trained generative models are taking the world by storm, due to their abilities in generating creative content.