Encoded Knowledge

Encoded knowledge research explores how large language models (LLMs) and other AI systems store and utilize information acquired during training. Current efforts focus on improving knowledge retrieval and reasoning capabilities, often employing techniques like prompt engineering, knowledge graph integration, and probabilistic reasoning within models such as transformers. This field is crucial for enhancing the reliability and safety of AI systems across diverse applications, from improving medical diagnosis accuracy to mitigating biases and vulnerabilities in LLM-based tools. The ultimate goal is to build AI systems that not only possess extensive knowledge but also demonstrate robust reasoning and reliable generalization across various tasks and domains.