Large language models (LLMs) like ChatGPT have taken the world by storm, but they're not without their flaws. One area where they've historically struggled is moral reasoning and avoiding harmful outputs. However, recent research suggests that LLMs possess an intrinsic self-correction capability, allowing them to refine their responses and align better with human values. A new paper digs deep into this ability, exploring how LLMs identify and correct issues like bias and toxicity in their outputs, even without explicit external feedback. The researchers found that LLMs improve their responses progressively through iterative interactions, gradually reducing harmful content and converging towards a stable, less toxic output. This self-improvement process, the study reveals, is driven by a fascinating interplay between "latent concepts" and "model uncertainty." When given instructions to be less biased or avoid harmful stereotypes, LLMs activate relevant latent concepts – the underlying moral orientations within the text they generate. Simultaneously, this activation process reduces the model's uncertainty about its responses, leading to more calibrated and accurate predictions over successive rounds of self-correction. This is because, by activating positive latent concepts like fairness, LLMs become increasingly confident in their ability to avoid toxic language. However, the study also found that the initial instruction plays a crucial role in how effectively an LLM self-corrects. A poorly crafted initial instruction may hinder an LLM’s ability to recognize and fix its mistakes. This research opens exciting possibilities for developing safer and more reliable AI systems. By better understanding the interplay between concepts and uncertainty, we may be able to design targeted strategies for boosting the self-correction capabilities of LLMs and make sure AI acts in alignment with our moral values. Further research will explore how to optimize these initial instructions and investigate the impact of external feedback on the self-correction process. This work represents an important step towards understanding the inner workings of LLMs and their remarkable ability to adapt and self-improve.