Large language models (LLMs) like ChatGPT are impressive, but they're not perfect. They can make factual errors, spout biases, and even generate toxic text. One promising area of research is getting LLMs to *self-correct*—to identify and fix their own flaws without constant human intervention. But is true self-correction an inherent capability of these models, or just a clever illusion? New research digs into the mechanisms of LLM self-correction, exploring how different techniques like chain-of-thought prompting and external feedback impact their ability to refine outputs, especially regarding moral and ethical issues. The findings reveal a complex interplay: while external feedback and chain-of-thought reasoning can individually improve LLM performance, combining these methods can create internal conflicts. The models sometimes struggle to reconcile external feedback with their internal knowledge, hindering the self-correction process. Furthermore, experiments show that LLMs are easily swayed by even weak interventions, suggesting that current self-correction methods are not robust. Perhaps most intriguing, the research introduces a 'self-distinguish' framework. This tests whether LLMs truly understand the *quality* of their outputs by asking them to choose between a better and worse response. The results suggest that LLMs can self-correct without necessarily grasping *why* one response is superior to another—they're fixing errors without fully comprehending the underlying moral and ethical landscape. These findings have important implications for how we develop and use LLMs. While true self-correction remains a challenge, this research suggests that targeted fine-tuning and a deeper understanding of the interplay between internal knowledge and external feedback are crucial for building more reliable and ethically sound AI systems. The quest for a truly self-correcting AI continues, but this research sheds light on the complexities of the journey.