OverleafCopilot: Empowering Academic Writing in Overleaf with Large Language Models
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Abstract (arXiv)
The rapid development of Large Language Models (LLMs) has facilitated a variety of applications from different domains. In this technical report, we explore the integration of LLMs and the popular academic writing tool, Overleaf, to enhance the efficiency and quality of academic writing. To achieve the above goal, there are three challenges: i) including seamless interaction between Overleaf and LLMs, ii) establishing reliable communication with the LLM provider, and iii) ensuring user privacy. To address these challenges, we present OverleafCopilot, the first-ever tool (i.e., a browser extension) that seamlessly integrates LLMs and Overleaf, enabling researchers to leverage the power of LLMs while writing papers. Specifically, we first propose an effective framework to bridge LLMs and Overleaf. Then, we developed PromptGenius, a website for researchers to easily find and share high-quality up-to-date prompts. Thirdly, we propose an agent command system to help researchers quickly build their customizable agents. OverleafCopilot (https://chromewebstore.google.com/detail/overleaf-copilot/eoadabdpninlhkkbhngoddfjianhlghb ) has been on the Chrome Extension Store, which now serves thousands of researchers. Additionally, the code of PromptGenius is released at https://github.com/wenhaomin/ChatGPT-PromptGenius. We believe our work has the potential to revolutionize academic writing practices, empowering researchers to produce higher-quality papers in less time.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 2 of its 5 references that are in the map. First posted on arXiv 13 Mar 2024.
Builds on
2 of 5 references.
References · 2
- 2023GPT-4 Technical Reportcited by 18,132
- 2022Training language models to follow instructions with human feedbackcited by 15,712
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Citing papers per year: 2025: 3; 2026: 4.
7 papers.
- 2025StructEval: Benchmarking LLMs' Capabilities to Generate Structural Outputscited by 23
- 2026InnoEval: On Research Idea Evaluation as a Knowledge-Grounded, Multi-Perspective Reasoning Problemcited by 6
- 2025PaperDebugger: A Plugin-Based Multi-Agent System for In-Editor Academic Writing, Review, and Editingcited by 5
- 2025SciGA: A Comprehensive Dataset for Designing Graphical Abstracts in Academic Paperscited by 2
- 2026pAI/MSc: ML Theory Research with Humans on the Loopcited by 2
- 2026How Researchers Navigate Accountability, Transparency, and Trust When Using AI Tools in Early-Stage Research: A Think-Aloud Studycited by 0
- 2026GenGA: Editable and Data-Grounded Graphical Abstract Generation for Academic Paperscited by 0
- 2026GenGA: Editable and Data-Grounded Graphical Abstract Generation for Academic Papers5 Aug 2026
- 2026How Researchers Navigate Accountability, Transparency, and Trust When Using AI Tools in Early-Stage Research: A Think-Aloud Study25 Apr 2026
- 2026pAI/MSc: ML Theory Research with Humans on the Loop22 Apr 2026
- 2026InnoEval: On Research Idea Evaluation as a Knowledge-Grounded, Multi-Perspective Reasoning Problem16 Feb 2026
- 2025PaperDebugger: A Plugin-Based Multi-Agent System for In-Editor Academic Writing, Review, and Editing2 Dec 2025
- 2025SciGA: A Comprehensive Dataset for Designing Graphical Abstracts in Academic Papers3 Jul 2025
- 2025StructEval: Benchmarking LLMs' Capabilities to Generate Structural Outputs26 May 2025