训练AI模拟科学记者，帮新手科研人员更好向公众讲清研究价值。
LLMs as Science Journalists: Supporting Early-stage Researchers in Communicating Their Science to the Public
- 用特定框架训练大模型扮演科学记者角色
- 能提出更关注社会影响的深层问题，引导研究者完善表达
- 用户测试中多数人更喜欢与该AI互动
科研界亟需工具帮助早期研究人员有效向公众传播其发现与创新。尽管现有通用大语言模型可提供协助，但其定位并不理想。为此，我们提出一个训练框架，使大模型能模拟科学记者角色，供早期研究者学习如何向公众准确传达论文内容。我们评估了所训练的LLM记者与模拟及真实研究者对话的实用性，并与通用模型对比。实验表明，采用本框架训练的模型能提出更相关的问题，聚焦研究的社会影响，促使研究者进一步澄清和扩展其成果。在用户研究中，多数参与者表示更偏好与本模型交流，而非通用大模型。
原文摘要 · Abstract (English)
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.
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