Large language models (LLMs) like ChatGPT are impressive, but they sometimes make mistakes that seem surprising. Imagine writing an email and choosing a slightly wrong word that changes the entire meaning. LLMs can fall into similar traps. New research introduces "Self-Evaluation Decoding" (SED), a method that helps LLMs avoid these pitfalls. Just like humans might rethink a decision, SED allows the model to "speculate" about the consequences of different word choices. It then evaluates these potential outcomes and picks the best one. This process mimics our own "slow thinking" approach to complex decisions. The results are promising: SED improves LLM performance across various tasks, including answering complex questions and solving math problems. It's like giving an LLM a dose of self-awareness, allowing it to double-check its work and produce more accurate and nuanced results. While SED shows great potential, there are challenges. The evaluation process adds complexity, making it slower than traditional methods. However, the researchers believe this trade-off is worthwhile, as it leads to significantly better answers. This research opens exciting avenues for improving LLM reliability and reasoning abilities. Imagine LLMs that can truly reflect on their output, leading to more insightful and trustworthy AI.