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Found 7 papers, 1 papers with code
Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks
Over the past decade, a large number of white-box adversarial robustness evaluation methods (i. e., attacks) have been proposed, ranging from single-step to multi-step methods and from individual to ensemble methods.
Controlled Automatic Task-Specific Synthetic Data Generation for Hallucination Detection
Hallucination pattern guidance leverages the most important task-specific hallucination patterns while language style alignment aligns the style of the synthetic dataset with benchmark text.
Learning from Contrastive Prompts: Automated Optimization and Adaptation
As LLMs evolve, significant effort is spent on manually crafting prompts.
Mutual Information Guided Optimal Transport for Unsupervised Visible-Infrared Person Re-identification
In the training stage, we utilize this matching information to introduce prototype-based contrastive learning for minimizing the intra- and cross-modality entropy ("Sharpness").
Orangutan: A Multiscale Brain Emulation-Based Artificial Intelligence Framework for Dynamic Environments
Achieving General Artificial Intelligence (AGI) has long been a grand challenge in the field of AI, and brain-inspired computing is widely acknowledged as one of the most promising approaches to realize this goal.
Efficient Continual Pre-training for Building Domain Specific Large Language Models
We further explore simple but effective data selection strategies for continual pre-training.
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Predictions
More and more investors and machine learning models rely on social media (e. g., Twitter and Reddit) to gather real-time information and sentiment to predict stock price movements.
