Stanford's 'Verbalized Sampling' Recovers the Creativity That RLHF Crushed — Without Retraining

A new Stanford paper demonstrates a training-free prompting method that restores the output diversity of base models in aligned LLMs like GPT-4, challenging the assumption that alignment permanently narrows model behavior.