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
https://github.com/X-PLUG/mPLUG-DocOwl/tree/main/PaperOwl.
Strengthening Multi-Modal Diagram Analysis for Scientific Academic Paper Writing
In the field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities, the ability to analyze diagrams has been a significant challenge for large language models (LLMs) and multimodal LLMs. However, a recent development in the creation ability of LLMs has paved the way for tools that assist in paper reading and writing. This article presents a novel approach that aims to enhance the diagram analysis abilities of multimodal LLMs, particularly in the context of scientific academic paper writing.
The authors have developed a dataset called M-Paper, which is designed to improve the multi-modal diagram understanding capabilities of LLMs. The dataset is created by parsing Latex source files of high-quality papers and aligning diagrams with related paragraphs. This allows for the construction of professional diagram analysis samples that can be used for training and evaluation purposes. Notably, 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.
To further enhance the alignment between the copilot and user’s intention, the authors introduce the concept of an ‘outline’ as a control signal. This outline can be provided by the user or generated automatically and then revised accordingly. The inclusion of this control signal aims to improve the overall performance of the copilot.
The research team conducted comprehensive experiments using a state-of-the-art multimodal LLM and trained it on their dataset. The results demonstrated a stronger scientific diagram understanding performance, encompassing diagram captioning, diagram analysis, and outline recommendation.
This work is highly interdisciplinary, bridging the fields of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. By addressing the limitations of LLMs and multimodal LLMs in diagram analysis, this research opens up new possibilities for leveraging the power of large language models in the context of academic paper writing. The availability of the dataset, code, and model on GitHub enables further research and development in this area.
Overall, the contribution of this research lies in its efforts to enhance the capabilities of LLMs and multimodal LLMs in understanding scientific diagrams, thereby assisting researchers and authors in the process of academic paper writing. By combining the strengths of multimedia information systems and language models, this work paves the way for more efficient and effective knowledge dissemination and communication in the scientific community.