Semantic Affinity

Semantic affinity research focuses on quantifying and leveraging the relationships between concepts, whether represented as words, images, or other data modalities. Current efforts concentrate on developing methods to measure and utilize this affinity for tasks like improving large language model reasoning, unsupervised image segmentation, and fair hate speech detection, often employing techniques like contrastive learning, Bayesian algorithms, and transformer-based architectures. These advancements have implications for various fields, including improving the interpretability and fairness of machine learning models, enhancing information retrieval, and facilitating more nuanced understanding of complex data.