Large language models (LLMs) are revolutionizing how we interact with technology, but they face a hidden challenge: efficiently exploring the vast search space of possible responses. Imagine an LLM trying to complete a sentence. Each word it adds opens up a branching tree of possibilities, quickly exploding into an overwhelming number of potential paths. Traditional search algorithms, like beam search, can be myopic, only looking one step ahead and potentially missing the best overall response. This research introduces a new approach called Uncertainty-Guided Likelihood-Tree Search (ULTS), a smarter way for LLMs to navigate this complex decision-making process. ULTS uses a probabilistic framework, essentially placing bets on which paths are most promising based on the LLM's own predictions. By considering the uncertainty inherent in language generation, ULTS efficiently guides the search towards high-likelihood sequences, minimizing wasted effort on less promising avenues. This non-myopic approach means ULTS considers not just the next word, but also the potential rewards of future words down the line. What sets ULTS apart from other non-myopic methods like Monte Carlo tree search is its efficiency. It doesn't require computationally expensive simulations, relying instead on readily available probability samples. This makes it significantly faster and more practical for real-world applications. Experiments show ULTS generates higher-quality text than current methods while requiring fewer calculations. ULTS offers a glimpse into a future where LLMs can respond more intelligently and efficiently to our prompts, opening doors to even more advanced and creative language applications. From chatbots to code generation, ULTS could enhance the performance and efficiency of a variety of tasks, allowing us to unlock the true potential of LLMs.