Semantic Uncertainty

Semantic uncertainty, the ambiguity or lack of confidence in the meaning of model outputs, is a critical challenge in various fields, particularly natural language processing and computer vision. Current research focuses on developing methods to quantify and mitigate this uncertainty, employing techniques like Bayesian inference, entropy calculations (including quantum entropy), and graph-based approaches to analyze semantic relationships within model predictions and responses. Addressing semantic uncertainty is crucial for improving the reliability and trustworthiness of AI systems, enabling safer and more robust applications in areas such as autonomous navigation, human-AI interaction, and knowledge-grounded dialogue.