Recently, the strong text creation ability of Large Language Models(LLMs) has
given rise to many tools for assisting paper reading or even writing. However,
the weak diagram analysis abilities of LLMs or Multimodal LLMs greatly limit
their application scenarios, especially for scientific academic paper writing.
In this work, towards a more versatile copilot for academic paper writing, we
mainly focus on strengthening the multi-modal diagram analysis ability of
Multimodal LLMs. By parsing Latex source files of high-quality papers, we
carefully build a multi-modal diagram understanding dataset M-Paper. By
aligning diagrams in the paper with related paragraphs, we construct
professional diagram analysis samples for training and evaluation. M-Paper is
the first dataset to support joint comprehension of multiple scientific
diagrams, including figures and tables in the format of images or Latex codes.
Besides, to better align the copilot with the user's intention, we introduce
the `outline' as the control signal, which could be directly given by the user
or revised based on auto-generated ones. Comprehensive experiments with a
state-of-the-art Mumtimodal LLM demonstrate that training on our dataset shows
stronger scientific diagram understanding performance, including diagram
captioning, diagram analysis, and outline recommendation. The dataset, code,
and model are available at
this https URL

通过强化多模态图表分析能力，我们构建了多模态图表理解数据集 M-Paper 并引入了控制信号‘outline’，通过与先进的多模态语言模型的全面实验，证明了在我们的数据集上进行训练能够展现更强大的科学图表理解性能.