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

2026-01-09

arXiv.org

AI Summary

This work addresses the challenge that researchers often lack effective tools to clearly communicate their findings to the public, a task at which general-purpose large language models (LLMs) exhibit inadequate alignment. The authors propose the first LLM training framework specifically designed for scientific communication, casting the model in the role of a “science journalist.” Rather than merely generating popular science texts, the framework employs conversational questioning to guide researchers in articulating the core findings and societal implications of their work. It integrates fine-tuning with an interactive prompting mechanism and is evaluated through both simulated and real-user experiments. Results demonstrate that the model generates more relevant and insightful questions, and users significantly prefer its interactive experience over that of general-purpose LLMs.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• science communication
• early-stage researchers
• large language models
• public engagement
• scientific outreach

Innovation

• science communication
• large language models
• LLM fine-tuning
• science journalism
• public engagement