Semantic Representation

Semantic representation focuses on encoding the meaning of text, images, or other data into numerical vectors that capture relationships and context. Current research emphasizes improving the accuracy and efficiency of these representations, particularly through advancements in large language models (LLMs), graph neural networks, and contrastive learning methods, often addressing issues like semantic leakage, hubness, and context length limitations. These improvements are crucial for various applications, including cross-lingual information retrieval, hate speech detection, zero-shot learning, and enhancing the factual accuracy and semantic consistency of large language model outputs. The resulting advancements in semantic representation are driving progress across numerous fields, from natural language processing and computer vision to robotics and scientific information extraction.