The scientific community needs tools that help early-stage researchers effectively communicate their findings and innovations to the public. Although existing general-purpose Large Language Models (LLMs) can assist in this endeavor, they are not optimally aligned for it. To address this, we propose a framework for training LLMs to emulate the role of a science journalist that can be used by early-stage researchers to learn how to properly communicate their papers to the general public. We evaluate the usefulness of our trained LLM Journalists in leading conversations with both simulated and human researchers. %compared to the general-purpose ones. Our experiments indicate that LLMs trained using our framework ask more relevant questions that address the societal impact of research, prompting researchers to clarify and elaborate on their findings. In the user study, the majority of participants who interacted with our trained LLM Journalist appreciated it more than interacting with general-purpose LLMs.

翻译：科学界需要能够帮助早期研究者有效向公众传播其研究成果与创新的工具。尽管现有的通用大型语言模型（LLMs）可为此提供协助，但其功能尚未实现最优适配。为此，我们提出一个训练大型语言模型模拟科学记者角色的框架，该框架可供早期研究者学习如何向普通公众恰当传达其论文内容。我们通过模拟研究者及真人研究者的引导式对话，评估了经我们训练的大型语言模型记者的实用性。实验表明，采用本框架训练的大型语言模型能提出更具相关性的问题，这些问题聚焦于研究的社会影响，从而促使研究者澄清并详述其发现。在用户研究中，大多数与经训练的大型语言模型记者交互的参与者表示，相较于通用大型语言模型，他们更认可该系统的价值。