Formatting academic manuscripts in LaTeX can be both laborious and technically demanding—especially when converting raw text into structured, publication-ready documents. This post presents a practical workflow using GPT-4.1 to automate manuscript formatting with remarkable precision. With a single, well-crafted prompt, the model generates clean LaTeX code with proper sectioning, abstract environments, author blocks, and placeholders for figures and references. The result is a fully compilable manuscript in Overleaf, produced without manual intervention. This workflow illustrates how generative AI can meaningfully reduce the technical overhead of academic writing.
Input file
To explore the potential of generative AI in automating manuscript formatting, we used the pre-publication version of one of our own peer-reviewed articles. The manuscript—prepared in standard text format—served as a realistic input example, containing title, author information, abstract, section headings, and unformatted body text.
The original study was later published as:
Sebők, M., Máté, Á., Ring, O., Kovács, V., & Lehoczki, R. (2025). Leveraging Open Large Language Models for Multilingual Policy Topic Classification: The Babel Machine Approach. Social Science Computer Review, 43(2), 295–317. https://doi.org/10.1177/08944393241259434
By using the manuscript version rather than the formatted PDF or typeset publisher version, we were able to evaluate the model’s ability to infer structure and produce valid LaTeX output from raw academic content—just as a researcher might do when preparing a submission or resubmission.
Prompt
To generate the LaTeX version of the manuscript, we designed a targeted prompt that clearly specified the required structure, including abstract, sections, and author block formatting. The prompt also instructed the model to return output that could be directly compiled in LaTeX, with no need for manual corrections.
Task
You are a professional LaTeX converter for academic manuscripts.
Convert the attached PDF article into a fully structured and clean LaTeX document using the article class.
Output requirements:
- Add \title{} and \author{} commands using the PDF's metadata
- Include the \begin{abstract} environment
- Convert each logical section of the article (Introduction, Methods, Results, etc.) into appropriate \section{} or \subsection{} commands
- Escape LaTeX special characters properly (%, _, &, $, #, {, }, etc.)
- Add [FIGURE HERE] or [TABLE HERE] placeholders where necessary
- Include references at the end using \section{References} or thebibliography environment (no need to match citation styles)
- Clean out journal headers, footers, or page numbers
Additional instructions:
- Use readable LaTeX formatting and indentations
- If a figure or table cannot be extracted, indicate its position with a placeholder
- Return the full LaTeX source code suitable for saving into a .tex file
The PDF to be converted is titled:
"Leveraging Open Large Language Models for Multilingual Policy Topic Classification: The Babel Machine Approach"
by Miklós Sebők, Ákos Máté, Orsolya Ring, Viktor Kovács, and Richárd Lehoczki, published in Social Science Computer Review (2024).
The full PDF is attached to this prompt.
Output
The resulting LaTeX output generated by GPT-4.1 was surprisingly clean and well-structured. The model correctly identified the abstract, sections, authorship information, and even formatted the metadata in line with academic conventions.
We pasted the output directly into Overleaf, a popular online LaTeX editor, and the document compiled successfully with no additional corrections required. The final result closely mirrored a professionally typeset manuscript, a clear demonstration of how generative AI can streamline the often tedious formatting stage of academic publishing.
Recommendations
Based on this experiment, we strongly recommend integrating large language models like GPT-4.1 into the academic writing workflow—particularly for automating formatting tasks. The model can reliably generate clean, structured LaTeX code that meets publication standards when provided with a clearly defined prompt. This not only reduces time spent on manual typesetting but also lowers the technical barrier for researchers unfamiliar with LaTeX. We suggest this approach especially for early-stage drafts, resubmissions, or converting plain-text manuscripts into submission-ready documents with minimal effort.
The authors used GPT-4.1 [OpenAI (2025), GPT-4.1 (accessed on 5 June 2025), Large language model (LLM), available at: https://openai.com] to generate the output.
