Knowledge Identification

Knowledge identification focuses on automatically detecting and extracting relevant information from diverse sources, including text, images, and multi-lingual data. Current research heavily utilizes large language models (LLMs), often employing self-training techniques or multi-agent frameworks to improve accuracy and efficiency, particularly in addressing challenges like data scarcity and hallucination. This field is crucial for advancing areas like teacher professional development, visual question answering, and cross-lingual information retrieval, ultimately aiming to create more robust and reliable AI systems capable of handling complex information needs.