Human Learning by Model Feedback: The Dynamics of Iterative Prompting
  with Midjourney
Shachar Don-Yehiya Leshem Choshen Omri Abend
Generating images with a Text-to-Image model often requires multiple trials,
where human users iteratively update their prompt based on feedback, namely the
output image. Taking inspiration from cognitive work on reference games and
dialogue alignment, this paper analyzes the dynamics of the user prompts along
such iterations. We compile a dataset of iterative interactions of human users
with Midjourney. Our analysis then reveals that prompts predictably converge
toward specific traits along these iterations. We further study whether this
convergence is due to human users, realizing they missed important details, or
due to adaptation to the model's ``preferences'', producing better images for a
specific language style. We show initial evidence that both possibilities are
at play. The possibility that users adapt to the model's preference raises
concerns about reusing user data for further training. The prompts may be
biased towards the preferences of a specific model, rather than align with
human intentions and natural manner of expression.