Lingshu Zeng

Transformers in Pseudo-Random Number Generation: A Dual Perspective on Theory and Practice

Pseudo-random number generators (PRNGs) are high-nonlinear processes, and they are key blocks in optimization of Large language models. Transformers excel at processing complex nonlinear relationships. Thus it is reasonable to generate high-quality pseudo-random numbers based on transformers. In thi