  Science journalism reports current scientific discoveries to non-specialists,
aiming to enable public comprehension of the state of the art. However, this
task can be challenging as the audience often lacks specific knowledge about
the presented research. To address this challenge, we propose a framework that
integrates three LLMs mimicking the real-world
writing-reading-feedback-revision workflow, with one LLM acting as the
journalist, a smaller LLM as the general public reader, and the third LLM as an
editor. The journalist's writing is iteratively refined by feedback from the
reader and suggestions from the editor. Our experiments demonstrate that by
leveraging the collaboration of two 7B and one 1.8B open-source LLMs, we can
generate articles that are more accessible than those generated by existing
methods, including advanced models such as GPT-4.
