# Diversity-restoring decoding and prompting > Source-anchored research for Diversity-restoring decoding and prompting. Run status: COMPLETED. Parent: [Why LLMs converge on the same words: mode collapse and post-training lexical effects](https://llms-explorer.com/tree/ai-isms-origins/) · 3 facets · 5 facts · page: https://llms-explorer.com/tree/diversity-restoring-decoding-and-prompting/ ## Definitions - Verbalized Sampling is a training-free prompt that asks the model to verbalize a probability distribution over multiple responses (e.g. 'Generate 5 jokes about coffee and their corresponding probabilities') to circumvent mode collapse. (high confidence) — [source](https://arxiv.org/abs/2510.01171) ## Comparisons and alternatives - VS authors attribute mode collapse to typicality bias in preference data and report 1.6-2.1x creative-writing diversity over direct prompting without hurting factual accuracy or safety; results are author-reported. (medium confidence) (verified as of 2026-07-01) — [source](https://arxiv.org/abs/2510.01171) - VS gains are larger for more capable models than for smaller mini/flash variants. (medium confidence) — [source](https://arxiv.org/abs/2510.01171) - A reanalysis (Schaeffer et al., June 2025) finds min-p does not beat baseline samplers on quality, diversity or their trade-off once hyperparameter count is controlled, cites omitted human-eval data and inconsistent LLM-judge reporting, and notes adoption figures were unsubstantiated and removed. (medium confidence) — [source](https://arxiv.org/abs/2506.13681) ## Facts and statements - Min-p sampling scales the truncation threshold by the top token's probability; the original paper claims better quality and diversity than top-p at high temperature and reports adoption in Hugging Face Transformers and vLLM. (medium confidence) — [source](https://arxiv.org/abs/2407.01082)