Semantic Continuity

Semantic continuity, the consistent and coherent representation of meaning across related inputs or time steps, is a crucial aspect of building trustworthy and interpretable AI systems. Current research focuses on quantifying this continuity, particularly in explainable AI (XAI) methods and large language models (LLMs), using metrics that assess the similarity of explanations or outputs for similar inputs. This work is driven by the need to improve model reliability and transparency, addressing issues like the generation of harmful content in LLMs and improving the accuracy of weakly supervised learning tasks such as image segmentation. Understanding and enhancing semantic continuity is vital for advancing AI's capabilities and ensuring its responsible development.