Large language models (LLMs) like ChatGPT are impressive, but they sometimes make things up—a problem known as "hallucination." Researchers are exploring how to make LLMs more reliable by enabling them to evaluate and correct their own work. This "self-feedback" process involves LLMs examining their responses, identifying inconsistencies, and refining their outputs. Imagine an LLM generating multiple answers to a question and then using a voting system to pick the best one. Or, picture it catching its own logical flaws by generating contradictory statements and resolving them. This research delves into various "self-feedback" techniques, from checking simple answer consistency to analyzing the complex inner workings of the LLM. While some argue that LLMs are better at generating text than judging it, evidence shows that self-feedback can improve accuracy, especially on factual tasks within the model's training data. Self-feedback won't magically solve all LLM problems, but it's a promising step toward building more trustworthy and self-aware AI. One of the key challenges lies in the paradox of reasoning: LLMs use both quick, intuitive reasoning and slower, deliberate thought processes, and finding the right balance is crucial for efficient problem-solving. Moving beyond simply generating text, future research will dive deeper into how LLMs process information, potentially leading to more universal and robust self-correction methods.