Implicit Knowledge

Implicit knowledge, the information implicitly encoded within models and data rather than explicitly stated, is a burgeoning area of research focusing on understanding how this knowledge is acquired, represented, and utilized. Current work investigates implicit knowledge in large language models (LLMs) and other deep learning architectures, exploring techniques like knowledge distillation, prompt engineering, and embedding manipulation to access and control this information. This research is crucial for improving model performance, mitigating biases, enhancing explainability, and addressing safety concerns in AI systems, with implications for various applications including natural language processing, computer vision, and decision-making.