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. to prompt a language
model with a few text-based genotypes (such as code, plain-text sentences, or equations) , and
to parse its corresponding output as those genotypes’ offspring. The promise of such language model crossover
(which is simple to implement and can leverage many different open-source language models)  is that it
enables a simple mechanism to evolve semantically-rich text representations
(with few domain-specific tweaks) , and naturally benefits from current progress in language models.
Experiments in this paper highlight the versatility of language-model crossover, through evolving
binary bit-strings, sentences, equations, text-to-image prompts, and Python code. The conclusion
is that language model crossover is a flexible and effective method for evolving genomes representable as text.