DocGenome: An Open Large-scale Scientific Document Benchmark for Training Next-generation Large Models

Scientific documents record research findings and valuable human knowledge, comprising a vast corpus of high-quality data. Thus, leveraging multi-modality data extracted from these documents and assessing large models' abilities to handle scientific document-oriented tasks is meaningful. Despite promising advancements, large models still perform poorly on multi-page scientific document extraction and understanding tasks, and their capacity to process within-document data formats such as charts and equations remains under-explored. To address these issues, we present DocGenome, a structured document dataset constructed by annotating 500K scientific documents from 153 disciplines in the arXiv open-access community, using our custom auto-labeling pipeline. DocGenome features four key characteristics:

• 1. Completeness: It is the first dataset to structure data from all modalities including 13 layout attributes along with their LaTeX source codes.
• 2. Logicality: It provides 6 logical relationships between different entities within each scientific document.
• 3. Diversity: It covers various document-oriented tasks, including document classification, visual grounding, document layout detection, document transformation, open-ended single-page QA and multi-page QA.
• 4. Correctness: It undergoes rigorous quality control checks conducted by a specialized team.

Besides, based on DocGenome, we conduct extensive experiments to demonstrate the advantages of DocGenome and objectively evaluate the performance of current large models on our benchmark.

This is Official Page of DocGenome Benchmark.