Semantic Distance

Semantic distance measures the similarity or dissimilarity between concepts, entities, or pieces of text, aiming to quantify their relatedness. Current research focuses on leveraging large language models and contrastive learning techniques to improve the accuracy and efficiency of semantic distance calculations, particularly within applications like audio captioning, robotics, and knowledge graph embeddings. These advancements are improving the evaluation of machine learning models, enabling more nuanced understanding of model behavior, and facilitating the development of more robust and equitable AI systems across various domains. The resulting improvements in semantic understanding have significant implications for tasks ranging from automated fact-checking to large-scale data analysis.