Elsevier

AI Open Template

Write in a clean editor, then format for AI Open in one click — DocuGuru applies the official Elsevier template with numbered references and exports a submission-ready PDF plus the editable LaTeX source. Free to start.

About the AI Open format

AI Open is a peer-reviewed journal published by Elsevier, covering Topic Modeling, Natural Language Processing Techniques, Advanced Graph Neural Networks.

PublisherElsevier
Reference styleNumbered (Elsevier)
Numbered — [1], [2] in the text
[1] A. Smith, B. Jones, C. Lee, A representative article title, AI Open 12 (2023) 45–58.

Formats any DOI in AI Open style. No sign-up.

Publishes research inTopic Modeling Natural Language Processing Techniques Advanced Graph Neural Networks Multimodal Machine Learning Applications Domain Adaptation and Few-Shot Learning
ISSN2666-6510
Citation impact (2-yr)6.43
h-index29
i10-index48
Total citations9,876
Article processing charge$800
Open accessYes
Top institutions publishing hereTsinghua University
Journal websitewww.sciencedirect.com
You getA submission-ready PDF and the editable LaTeX source — ready to submit.

Papers published in AI Open per year

6
2020
23
2021
19
2022
25
2023
19
2024
23
2025

Citation impact of AI Open by publication year

5.7K
2020
2K
2021
1.2K
2022
461
2023
226
2024
50
2025

Citations each year’s papers have accumulated so far — the most recent years are still building up.

Most-cited papers in AI Open

Graph neural networks: A review of methods and applications

Jie Zhou, Ganqu Cui, Shengding Hu et al. · 1 Jan 2020

Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics systems, learning molecular fingerprints, predicting protein interface, and classifying diseases demand a model to learn from graph inputs. In other domains such as learning from non-structural data like texts and images, reasoning on extracted structures (like the…

5,620 citations Cite SaveGo to paper →
Pre-trained models: Past, present and future

Xu Han, Zhengyan Zhang, Ning Ding et al. · 1 Jan 2021

Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved great success and become a milestone in the field of artificial intelligence (AI). Owing to sophisticated pre-training objectives and huge model parameters, large-scale PTMs can effectively capture knowledge from massive labeled and unlabeled data. By storing knowledge into huge parameters and fine-tuning on…

PTR: Prompt Tuning with Rules for Text Classification

Xu Han, Weilin Zhao, Ning Ding et al. · 1 Jan 2022

Recently, prompt tuning has been widely applied to stimulate the rich knowledge in pre-trained language models (PLMs) to serve NLP tasks. Although prompt tuning has achieved promising results on some few-class classification tasks, such as sentiment classification and natural language inference, manually designing prompts is cumbersome. Meanwhile, generating prompts automatically is also difficult and time-consuming.…

Data augmentation approaches in natural language processing: A survey

Bohan Li, Yutai Hou, Wanxiang Che · 1 Jan 2022

As an effective strategy, data augmentation (DA) alleviates data scarcity scenarios where deep learning techniques may fail. It is widely applied in computer vision then introduced to natural language processing and achieves improvements in many tasks. One of the main focuses of the DA methods is to improve the diversity of training data, thereby helping…

Lawformer: A pre-trained language model for Chinese legal long documents

Chaojun Xiao, Xueyu Hu, Zhiyuan Liu et al. · 1 Jan 2021

Legal artificial intelligence (LegalAI) aims to benefit legal systems with the technology of artificial intelligence, especially natural language processing (NLP). Recently, inspired by the success of pre-trained language models (PLMs) in the generic domain, many LegalAI researchers devote their effort to applying PLMs to legal tasks. However, utilizing PLMs to address legal tasks is still…

AI Open template — frequently asked questions

How do I write a paper in the AI Open format?
In DocuGuru you write your manuscript in a normal editor — no LaTeX setup required — and select the AI Open template. When you export, DocuGuru compiles the paper into the official Elsevier format and hands you a submission-ready PDF along with the editable LaTeX source.
What reference style does AI Open use?
AI Open uses Numbered (Elsevier) references, shown as numbered [1], [2] markers in the text. DocuGuru formats every in-text citation and the reference list in this exact style automatically. A reference appears like this: [1] A. Smith, B. Jones, C. Lee, A representative article title, AI Open 12 (2023) 45–58.
Do I need to know LaTeX to submit to AI Open?
No. DocuGuru generates the elsarticle LaTeX class and compiles the PDF for you in the background, so you get a Elsevier-ready AI Open document without writing any LaTeX. If you do want it, the LaTeX source is included in the export.
Can I import an existing draft into the AI Open template?
Yes. Paste or upload your current manuscript — Word, LaTeX, Markdown, or plain text — and DocuGuru reflows it into the AI Open format with correct headings, figures, tables, and numbered citations.
Who publishes AI Open?
AI Open is a multidisciplinary journal published by Elsevier. DocuGuru's AI Open template matches Elsevier's official submission format.
Can I export a submission-ready AI Open PDF?
Yes — DocuGuru produces a PDF built with the official AI Open template (the elsarticle class) that is ready to submit to Elsevier, together with the matching LaTeX source files.
How much does the AI Open template cost?
You can start writing in the AI Open template for free. Exporting the final submission-ready AI Open PDF and LaTeX source is part of DocuGuru's paid plans — see the app for current pricing.
Use the AI Open template