4 accepted papers
Quantifying and understanding human-AI alignment in high-risk tasks such as traffic accident prediction is crucial for deployment of AI systems. Existing alignment studies, however, focus mostly on the static domain and neglect the importance of attentional processing. Here, we present Attention‑DAD
In subjective tasks, different individuals can have different correct answers for the same input—the ground truth is not fixed but rather determined by each user's personal perspective. Standard foundation models suppress this individual variation by producing population-averaged predictions; conver
Text classification in education, usually called auto-tagging, is the automated process of assigning relevant tags to educational content, such as questions and textbooks. However, auto-tagging suffers from a data scarcity problem, which stems from two major challenges: 1) it possesses a large tag s…
Current supervised methods for facial landmark detection require a large amount of training data and may suffer from overfitting to specific datasets due to the massive number of parameters. We introduce a semi-supervised method in which the crucial idea is to first generate implicit face knowledge…