# Integrating curation into scientific publishing to train AI models

arXiv: https://arxiv.org/abs/2310.20440 · HTML: https://mlsanity.com/paper/2310.20440/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 66 references that are in the map.
- First posted on arXiv 31 Oct 2023.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [LinkBERT: Pretraining Language Models with Document Links](https://mlsanity.com/paper/2203.15827/) (2022)
- [BioRED: A Rich Biomedical Relation Extraction Dataset](https://mlsanity.com/paper/2204.04263/) (2022)
- [Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Again](https://mlsanity.com/paper/2203.08410/) (2022)
- [BioMegatron: Larger Biomedical Domain Language Model](https://mlsanity.com/paper/2010.06060/) (2020)
- [LLM-powered Data Augmentation for Enhanced Cross-lingual Performance](https://mlsanity.com/paper/2305.14288/) (2023)
- [BigBIO: A Framework for Data-Centric Biomedical Natural Language Processing](https://mlsanity.com/paper/2206.15076/) (2022)
- [Memorisation versus Generalisation in Pre-trained Language Models](https://mlsanity.com/paper/2105.00828/) (2021)
- [Large Language Models for Healthcare Data Augmentation: An Example on Patient-Trial Matching](https://mlsanity.com/paper/2303.16756/) (2023)
- [Named Entity Recognition with Partially Annotated Training Data](https://mlsanity.com/paper/1909.09270/) (2019)
- [A Two-stage Framework for Compound Figure Separation](https://mlsanity.com/paper/2101.09903/) (2021)
- [How Do Your Biomedical Named Entity Recognition Models Generalize to Novel Entities?](https://mlsanity.com/paper/2101.00160/) (2021)
- [Improving Small Language Models on PubMedQA via Generative Data Augmentation](https://mlsanity.com/paper/2305.07804/) (2023)
- [Informed Named Entity Recognition Decoding for Generative Language Models](https://mlsanity.com/paper/2308.07791/) (2023)
- [Partial Annotation Learning for Biomedical Entity Recognition](https://mlsanity.com/paper/2305.13120/) (2023)

## Built on by (most cited first, 1 of 1)

- [A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers](https://mlsanity.com/paper/2508.21148/) (2025)

## Abstract (arXiv)

High throughput extraction and structured labeling of data from academic articles is critical to enable downstream machine learning applications and secondary analyses. We have embedded multimodal data curation into the academic publishing process to annotate segmented figure panels and captions. Natural language processing (NLP) was combined with human-in-the-loop feedback from the original authors to increase annotation accuracy. Annotation included eight classes of bioentities (small molecules, gene products, subcellular components, cell lines, cell types, tissues, organisms, and diseases) plus additional classes delineating the entities' roles in experiment designs and methodologies. The resultant dataset, SourceData-NLP, contains more than 620,000 annotated biomedical entities, curated from 18,689 figures in 3,223 articles in molecular and cell biology. We evaluate the utility of the dataset to train AI models using named-entity recognition, segmentation of figure captions into their constituent panels, and a novel context-dependent semantic task assessing whether an entity is a controlled intervention target or a measurement object. We also illustrate the use of our dataset in performing a multi-modal task for segmenting figures into panel images and their corresponding captions.