intermediate~3h

Verbalized Sampling: Fixing LLM Mode Collapse

A training-free prompting technique that restores an aligned LLM's response diversity by asking it to verbalize a probability distribution over several answers instead of committing to one.

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📚 Prerequisites(1)

🎓 Learning objectives

  • Explain why RLHF-aligned models suffer from mode collapse and how typicality bias causes it
  • Apply the Verbalized Sampling prompt pattern to recover pre-trained response diversity
  • Quantify the diversity/quality tradeoff using reported benchmark results
  • Combine Verbalized Sampling with temperature and top-p for further diversity gains

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