用三个大模型模拟记者、读者、编辑的协作，让科普文章更易懂。
JRE-L: Journalist, Reader, and Editor LLMs in the Loop for Science Journalism for the General Audience
- 三模型循环：记者写稿，读者提疑问，编辑优化内容。
- 用7B和1.8B开源模型组合，效果优于GPT-4等单模型生成。
- 适合想提升科普内容可读性的研究者与媒体人。
科学新闻报道当前科研发现，旨在让非专业公众理解前沿进展。该任务挑战在于受众普遍缺乏相关知识背景。本文提出JRE-L框架，集成三个模仿写作-阅读-反馈-修订循环的LLM：一个作为记者，一个作为普通读者，一个作为编辑。记者撰写的稿件通过读者反馈与编辑建议进行迭代优化。实验表明，仅使用两个7B和一个1.8B的开源LLM，即可生成比现有方法（包括提示单个先进模型如GPT-4及其它多模型协作策略）更具可读性的文章。代码已公开于github.com/Zzoay/JRE-L。
原文摘要 · Abstract (English)
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 a JRE-L 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.
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