String Seed of Thought: Prompting LLMs for Distribution-Faithful and Diverse Generation

Sakana AI

Sakana AI discovered a prompting technique called String Seed of Thought (SSoT) that reduces output bias in large language models by instructing them to generate random strings internally before deriving answers. Testing across multiple LLMs showed SSoT achieved accuracy close to actual random sampling on reasoning models and improved diversity on the NoveltyBench benchmark across all six categories. The method requires only a small prompt addition with no external random number generator, making it applicable to content generation and ideation tasks where varied outputs are needed.