Large language models show great potential in generating and optimizing code. Widely used sampling methods such as Nucleus Sampling increase the diversity of generation but often produce repeated samples for low temperatures and incoherent samples for high temperatures. Furthermore, the temperature coefficient has to be tuned for each task, limiting its usability. We present Priority Sampling, a simple and deterministic sampling technique that produces unique samples ordered by the model's confidence. Each new sample expands the unexpanded token with the highest probability in the augmented search tree. Additionally, Priority Sampling supports generation based on regular expression that provides a controllable and structured exploration process. Priority Sampling outperforms Nucleus Sampling for any number of samples, boosting the performance of the original model from 2.87% to 5% improvement over -Oz. Moreover, it outperforms the autotuner used for the generation of labels for the training of the original model in just 30 samples.

大型语言模型在生成和优化代码方面展现出巨大潜力。我们提出了一种简单而确定性的采样技术——优先采样，它通过模型的置信度产生唯一的样本，从而在生成过程中改善了性能。优先采样还支持基于正则表达式的生成，提供了可控且有结构的探索过程。相比于Nucleus采样，优先采样在任意数量的样本中表现更好，将原始模型的性能提升了2.87%至5%的改进，并在仅仅30个样本中胜过用于原始模型训练标签生成的自动调参器。

编译器对大型语言模型的优先采样