Language Model Crossover: Variation through Few-Shot Prompting

Understand

This paper pursues the insight that language models naturally enable an intelligent variation operator similar in spirit to evolutionary crossover.

• In particular, language models of sufficient scale demonstrate in-context learning, i.e.
• they can learn from associations between a small number of input patterns to generate outputs incorporating such associations (also called few-shot prompting).
• This ability can be leveraged to form a simple but powerful variation operator, i.e.