Semantic Coverage

Semantic coverage, in the context of various machine learning models, focuses on ensuring that generated outputs (text, summaries, knowledge graphs, etc.) comprehensively represent the input data's meaning and relevant aspects. Current research emphasizes improving semantic coverage through techniques like federated learning for enhanced domain-specific instruction tuning, novel black-box testing methods based on co-domain coverage, and data-centric approaches that address issues like data age, quality, and domain heterogeneity in training datasets. These advancements are crucial for building more robust, reliable, and trustworthy AI systems across diverse applications, ranging from natural language processing and knowledge graph construction to autonomous systems and personalized dialogue agents.