New Knowledge

Research on incorporating new knowledge into large language models (LLMs) focuses on improving their ability to learn and adapt to constantly evolving information, addressing limitations in their static knowledge bases. Current efforts explore methods like supervised fine-tuning, retrieval augmented generation, and self-teaching strategies, often employing architectures such as BERT and GPT models, to enhance knowledge acquisition and retention while mitigating issues like catastrophic forgetting. This research is crucial for developing more robust and adaptable AI systems capable of handling real-world complexities and providing accurate, up-to-date information across diverse applications.