SciDoc2Diagrammer-MAF: Towards Generation of Scientific Diagrams from Documents guided by Multi-Aspect Feedback Refinement
<|reference_start|>SciDoc2Diagrammer-MAF: Towards Generation of Scientific Diagrams from Documents guided by Multi-Aspect Feedback Refinement: Automating the creation of scientific diagrams from academic papers can significantly streamline the development of tutorials, presentations, and posters, thereby saving time and accelerating the process. Current text-to-image models struggle with generating accurate and visually appealing diagrams from long-context inputs. We propose SciDoc2Diagram, a task that extracts relevant information from scientific papers and generates diagrams, along with a benchmarking dataset, SciDoc2DiagramBench. We develop a multi-step pipeline SciDoc2Diagrammer that generates diagrams based on user intentions using intermediate code generation. We observed that initial diagram drafts were often incomplete or unfaithful to the source, leading us to develop SciDoc2Diagrammer-Multi-Aspect-Feedback (MAF), a refinement strategy that significantly enhances factual correctness and visual appeal and outperforms existing models on both automatic and human judgement.<|reference_end|>
@article{mondal2024scidoc2diagrammer-maf:,
    title={SciDoc2Diagrammer-MAF: Towards Generation of Scientific Diagrams from
  Documents guided by Multi-Aspect Feedback Refinement},
    author={Ishani Mondal, Zongxia Li, Yufang Hou, Anandhavelu Natarajan, Aparna
  Garimella, Jordan Boyd-Graber},
    journal={Empirical Methods in Natural Language Processing 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.19242},
    primaryClass={cs.CL}
}