In recent years, machine learning has shown great promise in a variety of real-world applications.

We select a representative subset consisting of 22 datasets and propose it as the MESS benchmark.

Large language models are now tuned to align with the goals of their creators, namely to be "helpful and harmless." These models should respond helpfully to user questions, but refuse to answer requests that could cause harm. However, adversarial users can construct inputs which circumvent attempts at alignment. In this work, we study adversarial alignment, and ask to what extent these models remain aligned when interacting with an adversarial user who constructs worst-case inputs (adversarial examples). These inputs are designed to cause the model to emit harmful content that would otherwise be prohibited. We show that existing NLP-based optimization attacks are insufficiently powerful to reliably attack aligned text models: even when current NLP-based attacks fail, we can find adversarial inputs with brute force.

Score-based diffusion models (Song et al., 2021) have recently received significant attention within the

Categorical encoders transform categorical features into numerical representations that are indispensable for a wide range of machine learning models. Existing encoder benchmark studies lack generalizability because of their limited choice of 1. encoders, 2. experimental factors, and 3. datasets. Additionally, inconsistencies arise from the adoption of varying aggregation strategies. This paper is the most comprehensive benchmark of categorical encoders to date, including an extensive evaluation of 32 configurations of encoders from diverse families, with 48 combinations of experimental factors, and on 50 datasets. The study shows the profound influence of dataset selection, experimental factors, and aggregation strategies on the benchmark's conclusions -- aspects disregarded in previous encoder benchmarks.

Imitation learning uses data for training policies to solve complex tasks.

The data is collected in Peking University and uses the same data format as SemanticKITTI. To ensure all tasks are well-defined, we formalize consistent and compatible semantic class vocabulary across the above datasets, ensuring there is a one-to-one mapping between all semantic classes. As for ASFDA and ADA settings, we have an additional warm-up stage, i.e., the network is Both source and target data have a batch size of 16. Both training loss and validation loss consistently decrease over time, indicating effective model training. We report mIoU results across existing AL approaches in Table A3.

If the required memory to train a model exceeds this limit, the device will be excluded from the training.