Verbalized Sampling is a prompting strategy that asks LLMs to generate multiple candidate responses along with their probabilities, then samples from this distribution to improve output diversity. It works by instructing the model to verbalize its confidence and enumerate alternatives, reducing the tendency of LLMs to produce repetitive outputs. The approach is training-free and model-agnostic, functioning across GPT, Claude, Gemini, Llama, and other LLMs via prompting alone.