Injecting Context into LLMs

Large Language Models (LLMs) encode vast amounts of knowledge in their parameters; however, their internal knowledge is static, difficult to interpret, and not always reliable. This lecture provides an overview of existing and emerging techniques for extending, enriching, and controlling knowledge and context awareness in LLMs. Different categories of approaches will be discussed, highlighting their underlying mechanisms, strengths, and limitations. The lecture concludes by identifying open research challenges and outlining promising directions for more robust and controllable context awareness.