This parameter prompting, 5. Tobe t, instruction I: {Ii}n,It intoate{Ii}n refers in-conte It denotes wherethe54], we I into 6

The goal of data attribution for text-to-image models is to identify the training images that most influence the generation of a new image.

Bayesian optimization (BO) is a popular approach to optimize expensive-toevaluate black-box functions. A significant challenge in BO is to scale to highdimensional parameter spaces whileretaining sample efficiency. Asolution considered in existing literature is to embed the high-dimensional space in a lowerdimensional manifold, often via a random linear embedding.

We conduct a preliminary study on finding a proper prompt construction strategy. Further research can utilize our analysis to construct more powerful code prompts. Table 1: Code prompt study results of CBS. N" means there are one human-relevant function Table 2: Automatic and human evaluation results of social biases in the generated code on GPT -4. We also conduct experiments on GPT -4.

We propose a new paradigm to construct code prompts and successfully uncover social biases in code generation models.

A key problem in the application of Archimedean Copulas istheselection oftheparametric formofϕaswellasthelimitations onthe expressiveness of commonly used copula.

Nevertheless, despite theproliferation ofresearch onalgorithmic fairness inrecent years, veryfew methods exist that can handle multiclass classification tasks with non-binary sensitive attributes.