Law
Conformal Classification with Equalized Coverage for Adaptively Selected Groups
These sets reflect the model's confidence
Consent in Crisis: The Rapid Decline of the AI Data Commons
The web has become the primary communal source of data, or "data commons", for general-purpose
c2f71567cd53464161cab3336e8fc865-Paper-Datasets_and_Benchmarks_Track.pdf
Furthermore, extensive analysis ( 4.3) on Spider2-V demonstrate that these strategies remarkably
Harmony4D: A Video Dataset for In-The-Wild Close Human Interactions - Supplementary Materials - Rawal Khirodkar
For video demos of Harmony4D, please visit: Harmony4D Website. Please do not share the dataset with anyone as it is not publicly available yet. Harmony4D is a 75-minute video dataset collected using over 20 eqidistant, synchronized GoPro cameras. It consists of 1.66M images and 3.32M human instances, divided into 1.28M images for We manually clipped the videos into 208 sequences across 6 different activities, ensuring each sequence is at least 5 seconds (100 frames) long for temporal continuity. The 2D bboxes are derived from projected SMPL human vertices.
BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack
To bridge this gap, we introduce the BABILong benchmark, designed to test language models' ability to reason across facts distributed in extremely
RedCode: Risky Code Execution and Generation Benchmark for Code Agents
We then map the Python code to other programming languages (e.g., Bash) and natural text summaries or descriptions for
A Supplementary materials
The array geometry is shown in the RealMAN paper.
Large Language Model Unlearning
We study how to perform unlearning, i.e. forgetting undesirable (mis)behaviors, on large language models (LLMs).
The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models Hannah Rose Kirk 1
Human feedback is central to the alignment of Large Language Models (LLMs).
M3LEO: A Multi-Modal, Multi-Label Earth Observation Dataset Integrating Interferometric SAR and Multispectral Data
Satellite-based remote sensing has revolutionised the way we address global challenges in a rapidly evolving world. Huge quantities of Earth Observation (EO) data are generated by satellite sensors daily, but processing these large datasets for use in ML pipelines is technically and computationally challenging. Specifically, different types of EO data are often hosted on a variety of platforms, with differing degrees of availability for Python preprocessing tools. In addition, spatial alignment across data sources and data tiling for easier handling can present significant technical hurdles for novice users.