While extensive research on uncertainty estimation has been conducted with unimodal data, uncertainty estimation for multimodal data remains a challenge. Neural processes (NPs) have been demonstrated to be an effective uncertainty estimation method for unimodal data by providing the reliability of Gaussian processes with efficient and powerful DNNs.

Datasets are key to all aspects of machine learning (ML) and data science, from the development of novel algorithms to training of production models.

We explore uncertainty quantification in large language models (LLMs), with the goal to identify when uncertainty in responses given a query is large. We simultaneously consider both epistemic and aleatoric uncertainties, where the former comes from the lack of knowledge about the ground truth (such as about facts or the language), and the latter comes from irreducible randomness (such as multiple possible answers).

We study the implicit bias of optimization in robust empirical risk minimization (robust ERM) and its connection with robust generalization.

Generative Language Models (GLMs) have shown impressive performance in tasks such as text generation, understanding, and reasoning. However, the large model size poses challenges for practical deployment.

Therefore, our labels may contain errors. Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, tweets, other datasets)? If it links to or relies on external resources, a) are there guarantees that they will exist, and remain constant, over time; b) are there official archival versions of the complete dataset (i.e., including the external resources as they existed at the time the dataset was created); c) are there any restrictions (e.g., licenses, fees) associated with any of the external resources that might apply to a future user? Please provide descriptions of all external resources and any restrictions associated with them, as well as links or other access points, as appropriate.

Life is cautiously returning to the streets of Dilling, the second largest city in South Kordofan state, after the Sudanese army broke a suffocating siege that had isolated the area for more than two years. For months, the city had been encircled by the paramilitary Rapid Support Forces (RSF) and the Sudan People's Liberation Movement-North (SPLM-N), cutting off vital supply lines and trapping civilians in a severe humanitarian crisis. Al Jazeera Arabic's Hisham Uweit, reporting from Dilling, described a city "recovering slowly" from the economic strangulation. "For over two years, heavy siege conditions were imposed on the city. Movement disappeared, goods vanished and livelihoods narrowed," Uweit said.