Learning, Fast and Slow: Towards LLMs That Adapt Continually

The article discusses advancements in large language models (LLMs) that enable them to adapt continually, improving their learning processes. It highlights the importance of developing LLMs that can not only learn from vast amounts of data but also adjust to new information in real-time, enhancing their utility in various applications. This ongoing research aims to bridge the gap between static learning and dynamic adaptation in AI systems.