Knowledge Gap

Knowledge gaps, encompassing the discrepancies between existing knowledge and desired knowledge, are a central concern across various AI domains. Current research focuses on identifying and mitigating these gaps in large language models (LLMs) and other AI systems, employing techniques like knowledge graph integration, retrieval-augmented generation (RAG), and label-free learning methods to improve model accuracy and reliability. These efforts aim to enhance the trustworthiness and practical applicability of AI, particularly in fields like healthcare, education, and manufacturing, where reliable and robust AI systems are crucial. Addressing knowledge gaps is vital for advancing AI's capabilities and ensuring responsible deployment.