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MMBench-Video: A Long-Form Multi-Shot Benchmark for Holistic Video Understanding Xinyu Fang
The advent of large vision-language models (L VLMs) has spurred research into their applications in multi-modal contexts, particularly in video understanding.
Chasing Fairness Under Distribution Shift: A Model Weight Perturbation Approach
Fairness in machine learning has attracted increasing attention in recent years.
DAGER: Exact Gradient Inversion for Large Language Models
However, prior work has shown that the data can actually be recovered by the server using so-called gradient inversion attacks.
Embroid: Unsupervised Prediction Smoothing Can Improve Few-Shot Classification
Recent work has shown that language models' (LMs) prompt-based learning capabilities make them well suited for automating data labeling in domains where manual annotation is expensive.
MoGU: A Framework for Enhancing Safety of LLMs While Preserving Their Usability
Large Language Models (LLMs) are increasingly deployed in various applications. As their usage grows, concerns regarding their safety are rising, especially in maintaining harmless responses when faced with malicious instructions.
Certified Minimax Unlearning with Generalization Rates and Deletion Capacity
Gaussian mechanism borrowed from differential privacy.