Semantic Proximity

Semantic proximity focuses on quantifying the relational similarity between pieces of information, whether words, images, or sounds, to improve the performance of machine learning models. Current research emphasizes the impact of this proximity on tasks like machine translation, graph reasoning, and audio tagging, often leveraging large language models and graph convolutional networks to capture and utilize these relationships. Understanding and effectively incorporating semantic proximity is crucial for enhancing the accuracy and robustness of AI systems across various domains, leading to more human-like perception and improved performance in complex tasks.