We provide the guidelines presented to the users for the creation of the dataset. To see some examples of how the guidelines can be applied, visit the examples document. You can use it to rate each guideline and leave feedback for each task. The user should be allowed to refuse to give up any information. Ask the user to elaborate or rephrase instead.

Hyperparameter optimization is an important subfield of machine learning that focuses on tuning the hyperparameters of a chosen algorithm to achieve peak performance. Recently, there has been a stream of methods that tackle the issue of hyperparameter optimization, however, most of the methods do not exploit the dominant power law nature of learning curves for Bayesian optimization. In this work, we propose Deep Power Laws (DPL), an ensemble of neural network models conditioned to yield predictions that follow a power-law scaling pattern. Our method dynamically decides which configurations to pause and train incre-mentally by making use of gray-box evaluations.

It is worth noting thatSafe LoRAis a training-free and data-free approach, as it only requires the knowledge of the weights from the base and aligned LLMs.

Inthis work, we show that the reason for this deficiency is that language models are biased to learn word co-occurrence statistics instead of true factual associations.