verbalized-sampling

What problem does it solve?

Verbalized Sampling (VS) addresses mode collapse in LLM outputs by requesting a probability distribution over multiple responses, increasing diversity and coverage in creative tasks, open-ended QA, and prototyping scenarios.

Core Features & Use Cases

• Generate multiple candidate outputs with explicit probabilities for each result
• Applies across creative writing, brainstorming, persona simulation, synthetic data generation, adversarial examples, and open-ended QA
• Helps avoid the same-old cluster of ideas by encouraging tail and distribution-aware prompts

Quick Start

Prompt the model to produce k diverse outputs, each with a probability, and return them as a JSON array.