Hidden Semantics

Hidden semantics research focuses on uncovering and utilizing the underlying meaning and relationships within data, going beyond surface-level analysis. Current efforts concentrate on improving large language models (LLMs) and other neural networks to better capture these nuanced meanings, employing techniques like rationale distillation, soft negative sampling, and topological data analysis to enhance semantic representation and reasoning capabilities. This work is significant for advancing natural language processing, improving recommendation systems, and enabling more robust and interpretable AI systems across various domains, including computer vision and knowledge graph completion.