Education
ConStat: Performance-Based Contamination Detection in Large Language Models Jasper Dekoninck 1, Mark Niklas Müller 1,2, Martin Vechev 1 Department of Computer Science 1
Public benchmarks play an essential role in the evaluation of large language models.
Synatra: Turning Indirect Knowledge into Direct Demonstrations for Digital Agents at Scale
LLMs can now act as autonomous agents that interact with digital environments and complete specific objectives ( e.g., arranging an online meeting).
Y our contrastive learning problem is secretly a distribution alignment problem
Intuitively, by using more information from the distribution of latents, our approach allows a more distribution-aware manipulation of the relationships within augmented sample sets.
Aligner: Efficient Alignment by Learning to Correct
Aligner can be applied to any powerful, large-scale upstream models. Moreover, it can even iteratively bootstrap the upstream models using corrected responses as synthetic human preference data, breaking through the model's performance ceiling.
Advancing Video Anomaly Detection: A Concise Review and a New Dataset
Video Anomaly Detection (V AD) finds widespread applications in security surveillance, traffic monitoring, industrial monitoring, and healthcare.
ActFusion: a Unified Diffusion Model for Action Segmentation and Anticipation
Temporal action segmentation and long-term action anticipation are two popular vision tasks for the temporal analysis of actions in videos.
