Researchers from MIT and Peking University have introduced a self-correction mechanism aimed at improving the safety and reliability of large language models (LLMs). This innovation addresses the increasing tendency of LLMs to provide sensible yet incorrect answers as they scale up. The mechanism is part of a broader effort to enhance the reliability of AI systems, particularly as they grow in complexity and learn from human feedback. Reinforcement Learning from Human Feedback (RLHF) is also transforming the landscape of LLMs like GPT-3, focusing on feedback loops and best practices. A recent paper published in Nature highlights the need for a shift in AI design towards greater reliability, as scaling up LLMs has shown to decrease their reliability in answering simple questions.