LLMs as Science Journalists: Supporting Early-stage Researchers in Communicating Their Science to the Public

TL;DR

This paper develops a specialized framework for training large language models to act as science journalists, aiding early-stage researchers in effectively communicating their work to the public, and demonstrates its advantages over general-purpose models.

Contribution

The paper introduces a novel training framework for LLMs to emulate science journalists, improving public communication of scientific research by early-stage researchers.

Findings

Trained LLMs ask more relevant societal impact questions.

Participants preferred interactions with trained LLM Journalists.

Trained models enhance researcher communication clarity.

Abstract

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.