The sheer volume of scientific experimental results and complex technical
statements, often presented in tabular formats, presents a formidable barrier
to individuals acquiring preferred information. The realms of scientific
reasoning and content generation that adhere to user preferences encounter
distinct challenges. In this work, we present a new task for generating fluent
and logical descriptions that match user preferences over scientific tabular
data, aiming to automate scientific document analysis. To facilitate research
in this direction, we construct a new challenging dataset CTRLSciTab consisting
of table-description pairs extracted from the scientific literature, with
highlighted cells and corresponding domain-specific knowledge base. We
evaluated popular pre-trained language models to establish a baseline and
proposed a novel architecture outperforming competing approaches. The results
showed that large models struggle to produce accurate content that aligns with
user preferences. As the first of its kind, our work should motivate further
research in scientific domains.

我们提供了一个生成符合用户偏好的流畅且逻辑一致的科学表格数据描述的新任务，并通过构建一个具有高亮单元格和相应专业知识库的数据集，为这个方向的研究提供了基准，并提出了一种优于竞争方法的新架构。结果显示，大型模型在生成与用户偏好一致的准确内容方面存在困难。作为首创，我们的工作有望推动科学领域的进一步研究。