This limits these methods to a low-resolution representation and makes it difficult to scale up to the dense views for better quality.

Jailbreak attacks cause large language models (LLMs) to generate harmful, unethical, or otherwise objectionable content. Evaluating these attacks presents a number of challenges, which the current collection of benchmarks and evaluation techniques do not adequately address.

A Spoken Language Dataset of Descriptions for Speech - Based Gro unded Language Learning.

Data curation is a field with origins in librarianship and archives, whose scholarship and thinking on data issues go back centuries, if not millennia.

In Sec. 2 we include a We include a datasheet for our dataset following the methodology from "Datasheets for Datasets" Ge-17 In this section, we include the prompts from Gebru et al. [2021] in blue, and in For what purpose was the dataset created? Was there a specific task in mind? The dataset was created to facilitate research development on cloud removal in satellite imagery. Specifically, our task is more temporally aligned than previous benchmarks. Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., Who funded the creation of the dataset?

Clouds in satellite imagery pose a significant challenge for downstream applications. A major challenge in current cloud removal research is the absence of a comprehensive benchmark and a sufficiently large and diverse training dataset.

Moreover, there remains a considerable gap between the ability to answer exam questions and the application of this knowledge in real-world situations. To bridge the gap and thoroughly assess LLMs in supporting the crop science field, we introduce CROP.

The CROP benchmark includes 5,045 multiple-choice questions covering three difficulty levels.