面向大众的自动科学新闻报道中的LLM协作机制研究
LLM-Collaboration on Automatic Science Journalism for the General Audience
AI总结：
本研究针对科学新闻面向大众时的可读性难题，提出模拟写作-阅读-反馈-修改流程的三LLM协作框架，经实验验证其用中小参数开源模型生成的内容比GPT-4等方法更易懂。
摘要：
科学新闻向非专业人士报道最新科学发现，旨在帮助公众理解前沿研究进展。然而，由于受众通常缺乏所介绍研究的相关专业知识，这项任务颇具挑战性。为应对这一难题，我们提出一个整合3个大语言模型（LLM）的框架，模拟现实中“写作-阅读-反馈-修改”的工作流程：一个LLM担任记者，一个更小的LLM担任普通读者，第三个LLM担任编辑。记者的稿件会根据读者的反馈和编辑的建议进行迭代优化。实验表明，通过协同2个7B参数和1个1.8B参数的开源LLM，我们生成的文章比现有方法（包括GPT-4这类先进模型）生成的内容更通俗易懂。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
