aegis
Datasets
All datasets matching “aegis”Aegis-AI-Content-Safety-Dataset-2.0
🛡️ Nemotron Content Safety Dataset V2
The Nemotron Content Safety Dataset V2, formerly known as Aegis AI Content Safety Dataset 2.0, is comprised of 33,416 annotated interactions between humans and LLMs, split into 30,007 training samples, 1,445 validation samples, and 1,964 test samples. This release is an extension of the previously published Nemotron Content Safety Dataset V1.
To curate the dataset, we use the HuggingFace version of human preference data about harmlessness… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-2.0.Aegis-AI-Content-Safety-Dataset-1.0
🛡️ Nemotron Content Safety Dataset V1
Nemotron Content Safety Dataset V1, formerly known as Aegis AI Content Safety Dataset, is an open-source content safety dataset (CC-BY-4.0), which adheres to Nvidia's content safety taxonomy, covering 13 critical risk categories (see Dataset Description).
Dataset Details
Dataset Description
Nemotron Content Safety Dataset V1 is comprised of approximately 11,000 manually annotated interactions between humans and LLMs, split… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-1.0.Matterport3D-ScansAEGIS_dataset
Data Description
EEGRaw electroencephalography (EEG) data collected during VR driving tasks.
EEG_merged_cleanPreprocessed EEG datasets with artifacts removed and event markers aligned. This folder also includes MATLAB scripts used to compute and plot event-related potentials (ERP).
camera_imagesExample camera data captured in the CARLA simulator, including RGB, depth, and semantic images.
eye_tracking_driving_dataVehicle control signals recorded during driving, such as throttle… See the full description on the dataset page: https://huggingface.co/datasets/zzhuan/AEGIS_dataset.AEGISThis repository contains the data of the paper [AEGIS: A Holistic Benchmark for Evaluating Forensic Analysis of AI-Generated Academic Images]
AEGIS
AEGIS: Automated Error Generation and Identification for Multi-Agent Systems
Paper | Project Page | Code
AEGIS Framework
Overview
AEGIS (Automated Error Generation and Identification for Multi-Agent Systems) is a large-scale dataset and benchmark for detecting errors in Multi-Agent Systems (MAS). It addresses the critical lack of large-scale, diverse datasets with precise, ground-truth error labels for MAS, which has hampered research in understanding MAS… See the full description on the dataset page: https://huggingface.co/datasets/Fancylalala/AEGIS.