ExpStar: Towards Automatic Commentary Generation for Multi-discipline Scientific Experiments
Jiali Chen, Yujie Jia, Zihan Wu, Jinyu Yang, Jianpeng Chen, Xusen Hei, Jiayuan Xie, Yi Cai, Qing Li · 2025
Experiment commentary is crucial in describing the experimental procedures, delving into underlying scientific principles, and incorporating content-related safety guidelines. In practice, human teachers rely heavily on subject-specific expertise and invest significant time preparing such commentary. To address this challenge, we introduce the task of automatic commentary generation across multi-discipline scientific experiments. Current LMMs' ability to generate fine-grained and insightful experiment commentary remains largely under-explored. In this paper, we make the following contributions: (i) We construct ExpInstruct, the first dataset tailored for experiment commentary generation, featuring over 7 K step-level commentaries across 21 scientific subjects from 3 core disciplines. (ii) We propose ExpStar, an automatic experiment commentary generation model that leverages a retrieval-augmented mechanism to adaptively access, evaluate, and utilize external knowledge. (iii) Extensive experiments show that our ExpStar substantially outperforms 14 leading LMMs, which highlights the superiority of our dataset and model. We believe that ExpStar holds great potential for advancing AI-assisted scientific experiment instruction.