Fairness metrics are a core tool in the fair machine learning literature (FairML), used to determine that ML models are, in some sense, "fair."

The inferred user preference descriptions are used to define prompts for generating responses in the future.

Diffusion models have revolutionized customized text-to-image generation, allowing for efficient synthesis of photos from personal data with textual descriptions.

While this refusal behavior is widespread across chat models, its underlying mechanisms remain poorly understood. In this work, we show that refusal is mediated by a one-dimensional subspace, across 13 popular open-source chat models up to 72B parameters in size.

Data-driven decision making relies on estimating the effect of interventions, i.e. causal effect estimation . For example, a doctor must decide which medicine she will give her patient, ideally the one with the greatest effect on positive outcomes.

The current paradigm of evaluating Large Language Models (LLMs) through static benchmarks comes with significant limitations, such as vulnerability to data contamination and a lack of adaptability to the evolving capabilities of LLMs.

For what purpose was the dataset created? Was there a specific task in mind? Who created this dataset (e.g. which team, research group) and on behalf of which Who funded the creation of the dataset? This work is partially funded by Cisco and Amazon. What do the instances that comprise the dataset represent (e.g.

However, estimating MI between multidimensional variables is challenging because the number of samples necessary to converge to an accurate estimate scales unfavorably with dimensionality.

Fine urban change segmentation using multi-temporal remote sensing images is essential for understanding human-environment interactions in urban areas.