Q1 For what purpose was the dataset created? Q2 Who created the dataset (e.g., which team, research group) and on behalf of which Q3 Who funded the creation of the dataset? Q1 What do the instances that comprise the dataset represent (e.g., documents, photos, Are there multiple types of instances (e.g., movies, users, and ratings; Is the sample representative of the larger set (e.g., geographic coverage)? Q4 What data does each instance consist of? In either case, please provide a description.

Our framework is designed to be lightweight and automatic so that evaluation runs are cheap and fast. Our initial run evaluates 22 VLMs on 21 existing datasets to provide a holistic snapshot of the models. We uncover new key findings, such as the fact that efficiency-focused models (e.g., Claude 3 Haiku or Gemini 1.5 Flash) perform significantly

However, the availability of such resources remains scarce and evaluation faces added complexity due to reproducibility issues in experimental protocols.

California's landmark frontier AI law to bring transparency Late last month, California became the first state in the United States to pass a law to regulate cutting-edge AI technologies. Now experts are divided over its impact. They agree that the law, the Transparency in Frontier Artificial Intelligence Act, is a modest step forward, but it is still far from actual regulation. It mandates reporting of incidents such as large-scale cyber-attacks, deaths of 50 or more people, large monetary losses and other safety-related events caused by AI models. It also puts in place whistleblower protections.

The complexity of automating bias evaluation in textual content poses significant challenges. Traditional approaches in social sciences, such as content analysis, often rely on manual word counts from static lists [Gaucher et al., 2011], which may miss the subtleties and unlisted language cues that advanced NLP technologies can detect.

A VSR, respectively) has traditionally been conducted independently.

When turned on, "GitHub Copilot checks code completion suggestions with their surrounding

Each video in the dataset is paired with a question and four or five choices.