  Large language models are revolutionizing several areas, including artificial
creativity. However, the process of generation in machines profoundly diverges
from that observed in humans. In particular, machine generation is
characterized by a lack of intentionality and an underlying creative process.
We propose a method called Creative Beam Search that uses Diverse Beam Search
and LLM-as-a-Judge to perform response generation and response validation. The
results of a qualitative experiment show how our approach can provide better
output than standard sampling techniques. We also show that the response
validation step is a necessary complement to the response generation step.
