Attention Calibration

Attention calibration aims to improve the performance of deep learning models, particularly large language and vision-language models, by refining how these models focus their attention on input data. Current research focuses on identifying and mitigating issues like "attention sinks"—where disproportionate attention is given to less important information—and developing techniques to dynamically adjust attention weights during inference, often without requiring retraining. These advancements lead to improved accuracy and reduced hallucinations in tasks ranging from text generation and image captioning to medical image segmentation and speech emotion recognition, demonstrating the broad applicability of attention calibration across diverse domains.