Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity

---

Quickstart | Colab | Reproduce Results | Paper | Blog | Examples | Practical Tips | Podcast | Citation

Verbalized Sampling (VS) is a simple prompting strategy that improves LLM diversity by 2-3x. It works by asking the model to generate multiple responses with their probabilities, then sampling from this distribution. VS is training-free (works with any LLM via prompting), model-agnostic (GPT, Claude, Gemini, Llama, etc.), orthogonal to temperature, and effective across tasks like creative writing, social simulation, synthetic data generation, and open-ended QA.

Quickstart

To try Verbalized Sampling, just copy and paste this into any chatbot (ChatGPT, Claude, Gemini, etc.). For best results, we recommend starting with models like GPT-5, Claude 4 Opus, and Gemini 2.5 Pro:

<instructions>
Generate 5 responses to the user query, each within a separate <response> tag. Each <response> must include a <text> and a numeric <probability>.
Please sample at random from the tails of the distribution, such that the probability of each response is less than 0.10.
</instructions>

Tell me a short story about a bear.

If you want more stories, just respond and ask Tell me 5 more stories in the same conversation. For even better results, paste this into a system prompt instead:

You are a helpful assistant. For each query, please generate a set of five possible responses, each within a separate <response> tag. Each <response> must include a <text> and a numeric <probability>.
Please sample at random from the tails of the distribution, such that the probability of each response is less than 0.10.

For practical tips on getting the most out of this technique and general troubleshooting, please refer to this X/Twitter thread!

Installation and Usage

For all of the above in a single function call, the ability to automatically sample from the verbalized responses, and LangChain integration, use our Python package:

pip install verbalized-sampling

# Set OPENAI_API_KEY or OPENROUTER_API_KEY in bash
from verbalized_sampling import verbalize

# Generate distribution of responses
dist = verbalize("Tell me a joke", k=5, tau=0.10, temperature=0.9)

# Sample from the distribution
joke = dist.sample(seed=42)
print(joke.text)

Colab Notebooks

Here are some examples of how to use verbalized sampling for generating more diverse stories, ideas, images, and how to use our package:

Notebook	Description	Code	Run it Yourself!
Direct vs. Verbalized Sampling	Head-to-head comparison demonstrating VS effectiveness: 2-3x diversity improvement in creative tasks while maintaining quality	View on GitHub
Image Generation with VS	Visual comparison of Direct Prompting vs. Verbalized Sampling for text-to-image generation, showcasing creative diversity in artistic styles	View on GitHub
Complete Framework Tutorial	Step-by-step guide to using verbalized sampling: API basics, transforms, selection methods, recipes, and advanced features	View on GitHub

Reproducing Paper Results

Our library includes everything you need to reproduce the results from our paper. For example:

# Run creative writing experiments
python scripts/tasks/run_poem.py --model gpt-4.1 --methods direct vs_standard --num-responses 50

# Evaluate bias mitigation on geographic data
python scripts/tasks/run_state_name.py --model anthropic/claude-sonnet-4 --methods direct vs_standard

# Compare diversity metrics across methods
python scripts/tasks/run_story.py --model gpt-4.1 --methods direct vs_standard vs_cot --metrics diversity ngram

For complete experiment instructions with exact commands, parameter settings, and expected outputs, see EXPERIMENTS.md which provides 1-to-1 mapping between paper sections and experiment scripts.

Citation

If you use Verbalized Sampling in your research, please cite our paper:

@misc{zhang2025verbalizedsamplingmitigatemode,
  title={Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity},
  author={Jiayi Zhang and Simon Yu and Derek Chong and Anthony Sicilia and Michael R. Tomz and Christopher D. Manning and Weiyan Shi},
  year={2025},
  eprint={2510.01171},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2510.01171}
}

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.