Just one prompt can unlock a stronger version of the LLM.
When you ask a question, the model calculates multiple response options.
Among them are strong, strange, and groundbreaking ones.
But it almost never outputs them. Due to training through human feedback, the "mode collapse" effect occurs.
The default model defaults to safe, typical, and predictable responses. It knows a stronger option, but prioritizes the safe one.
Researchers described a way to bypass this filter. The method is called Verbalized Sampling.
If you ask for one response, the model selects the most likely one. If you ask to generate 5 options and specify the probability for each, the behavior changes.
The model starts exploring the "tails of the distribution". Instead of 99% predictable responses, less likely but stronger options appear.
In tests, this technique increased diversity and creativity by up to 2.1 times on top models.
Without losing accuracy and safety. 🤖
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