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OverleafCopilot: Empowering Academic Writing in Overleaf with Large Language Models

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arxiv 2403.09733 v1 pith:N4TJUVUL submitted 2024-03-13 cs.CL cs.AI

classification cs.CLcs.AI
keywords llmsoverleafresearcherswritingacademicoverleafcopilotchallengesempowering
verification ladder T0 review T1 audit T2 compute T3 formal

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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. TeXFix-Bench: An Empirically Grounded Multi-Format Benchmark for LLM-Based Document Source Repair

    cs.AI 2026-08 conditional novelty 6.0 of 10

    TeXFix-Bench grounds document-repair evaluation in a mined 18-category fault taxonomy, and shows that compile success alone misses content-destroying repairs in 13.6-18.5 percent of cases.

  2. GenGA: Editable and Data-Grounded Graphical Abstract Generation for Academic Papers

    cs.GR 2026-08 conditional novelty 6.0 of 10

    A vector-first framework, GenGA, generates editable graphical abstracts from paper text and user images, together with a Structural Independence Coefficient metric for editing simplicity.

  3. InnoEval: On Research Idea Evaluation as a Knowledge-Grounded, Multi-Perspective Reasoning Problem

    cs.CL 2026-02 conditional novelty 6.0 of 10

    Idea evaluation can be automated with LLM agents that retrieve heterogeneous online evidence, simulate diverse reviewers, and score ideas on multiple dimensions, outperforming existing judges on acceptance-label predi...

  4. MathSpeech: Leveraging Small LMs for Accurate Conversion in Mathematical Speech-to-Formula

    cs.CL 2024-12 conditional novelty 6.0 of 10

    A 120M-parameter, two-stage T5 pipeline corrects ASR errors on math speech and converts them to LaTeX, outperforming one-shot GPT-4o on a new MIT OCW-derived benchmark.

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