JRE - L : Journalist, Reader, and Editor LLM s in the Loop for Science Journalism for the General Audience (2025)

Abstract: Science journalism reports current scientific discoveries to non-specialists, aiming to enable public comprehension of the state of the art. This task is challenging as the audience often lacks specific knowledge about the presented research. We propose JRE-L, a framework that integrates three LLMs mimicking the writing-reading-feedback-revision loop. In JRE-L, one LLM acts as the journalist, another 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 prompting single advanced models such as GPT-4 and other LLM-collaboration strategies. Our code is publicly available at github.com/Zzoay/JRE-L.

## introduction science journalism creates news articles that cover a wide range of scientific research, enhancing the public's understanding of science @xcite @xcite . with rapid advances in various disciplines, science journalism struggles to keep pace with the exponential growth of knowledge. in response, automatic science journalism (asj) has been proposed to expedite the filtering, learning, and communication of scientific knowledge @xcite . the essence of asj lies in elucidating complex technical content for readers, thereby facilitating their comprehension of advanced research @xcite . the degree to which content is embraced depends on the reader's domain knowledge @xcite , thus scientific content may be challenging for the general reader, as illustrated in figure 1 @xcite . some researchers have developed parallel corpora @xcite @xcite , where the target content is extracted from online scientific news or journals. however, these press releases often remain technical, likely because they are originally tailored for researchers rather than the general audience. large language models (llms) have shown impressive proficiency in instruction adherence and content generation @xcite , thereby making them potential tools for asj. furthermore, llms have exhibited social intelligence, enabling them to play realistic roles and collaborate in real-world tasks @xcite @xcite . moreover, llms can iteratively improve their performance by context updates without training @xcite . motivated by these observations, we propose a com-municative and iterative framework that leverages llms to gradually accomplish the asj task. our goal is to automatically generate a popular science article based on a technical paper and improve the accessibility of the generated article to the general audience. in real-world journalism, a journalist typically receives and learns from revision suggestions from a professional editor @xcite , as reviews can lead to writing improvements @xcite . furthermore, in science communication, receiving feedback from the audience can improve scientists' communication skills @xcite . thus, the introduction of general readers' feedback into the journalism loop is promising to make the writing more accessible to the general public, as illustrated in @xcite . based on real-world practices and research results, we design the jre-l framework, in which three llms collaborate in a loop of writing, reading, feedback, and revision, to generate highly accessible popular science articles. concretely, we have an llm serve as the journalist writing for readers who lack domain knowledge of the article. to help expose writing problems that might hinder the reading experience of general readers, we have another llm, smaller than the journalist llm, to serve as a general reader. this reader llm reads the article written by the journalist and takes notes for giving feedback. as a less proficient model, the reader llm needs material that is easily understandable to take comprehensive notes. therefore, the more accessible the written article is, the greater the clarity and accuracy of the reader's notes will exhibit. llms have shown the capability of evaluating the quality of text @xcite @xcite . therefore, we let an editor (the third llm) evaluate the correctness and comprehensiveness of the reader's notes and then provide suggestions for the revision of the journalist's article. the journalist then revises the article based on the suggestions. by this iterative and parameter-free tuning process, the popular science article is enhanced and made more accessible to a general audience. to the best of our knowledge, our work is the first study on llm collaboration for asj. to assess our proposed method, we employ both automatic metrics and human evaluation on measures including readability, information con-veyance, authenticity, and interestingness of our generated articles. compared with other methods, including those with fine-tuning and prompting on various llms, our proposed method achieves the highest readability while remaining competitive on the other measures. we also provide a detailed analysis, including ablation studies of removing the editor llm, removing the reader llm, or removing both, as well as trend analysis and case studies, to offer a comprehensive understanding on llms in the asj task. in brief, we make the following contributions: ## methodology following asj @xcite ; @xcite ; @xcite ) aims to automatically distill a scientific paper into an article accessible to a broader audience. our asj framework jre-l employs an iterative workflow of writing, reading, suggestion-making, and revision among three llms, as illustrated in figure 2 . all prompts for each llm agent are listed in appendix a. ## conclusion this study proposes the collaboration of llms in the loop for asj aimed at general readers. initially, an llm functions as a journalist by composing an explanatory article for the general public. subsequently, another llm, acting as a general audience, reads these articles and takes notes, helping reveal the readability issues in the generated article. then, an editor llm assesses the reader's notes and offers suggestions for improvement. following the suggestions, the journalist revises its article, and then passes it to the reader to continue the iterative process. extensive experiments are conducted to evaluate the effectiveness of our framework, including both automatic and human evaluation. in comparison to prompting and fine-tuning llms as other asj systems do, our method achieves the highest readability while maintaining high quality.