OverleafCopilot: Empowering Academic Writing in Overleaf with Large Language Models (2403.09733v1)

Abstract: The rapid development of 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.

• The paper introduces OverleafCopilot, a novel solution that integrates LLM functionalities into Overleaf via a modular agent framework.
• It employs an XML-based Template Directive Engine and a Scoped Event Bus to efficiently generate and manage prompt-driven LLM actions.
• The architecture enhances academic writing through advanced UI customization, real-time backend services, and robust privacy safeguards.

The paper presents a comprehensive technical solution that integrates LLMs (LLMs LLMs) with Overleaf, a widely used collaborative academic writing platform. The work addresses key challenges in bridging Overleaf and LLMs by introducing a browser extension that not only enhances the efficiency of academic writing but also provides flexible customization through a modular agent system.

The proposed system is architected as follows:

• Seamless Integration of LLMs with Overleaf:

The extension, OverleafCopilot, is implemented as a Chrome extension and is designed to work directly inside the Overleaf environment. It provides a user interface that allows researchers to leverage functionalities such as paper polishing, grammar checking (for English and Chinese), translation, and writing suggestions. Each operation is supported by a corresponding agent, which abstracts specific LLM interactions. The system permits users to input their own API keys to connect with LLM providers (e.g., OpenAI) or use a pre-existing license-based access.

• Modular Agent and Template Directive Engine:

A core contribution of the work is the introduction of a Template Directive Engine (TDE). This engine enables users to define agents via an XML-like tree structure. Each agent is characterized by:

• A unique name and descriptive metadata (e.g., icons sourced from Material Design Icons).
• A set of directives that encapsulate user interaction, LLM prompt design, and pre-/post-action processing.

The agents follow a Perceive-Think-Act cycle:

• Pre-action: Tasks to be performed before the API call.
• Prompt Generation: Dynamically constructing prompts based on user input.
• API Call: Invoking LLMs using hard-coded or user-specified parameters such as temperature (e.g., $temperature=0.7$ where temperature regulates output randomness).
• Post-action: Operations that might, for instance, copy output to the clipboard or insert it into the Overleaf editor.

This agent-based framework is further empowered by the ability to customize shortcuts and integrate high-quality prompting via the dedicated PromptGenius website. Users are given the flexibility to tailor both the prompt contents and UI bindings to suit individual academic writing styles.

• Advanced Communication Framework via a Scoped Event Bus and MSC:

The architectural design leverages a multi-layer communication framework. Key components include:

• Scoped Event Bus (SEB): Implements a publish-subscribe model where events are hierarchically scoped. For example, an action like "layout.switch" triggers a cascade of events including generic, scoped, and finally dedicated events (e.g., an event chain such as “layout,” “layout.switch,” “layout.switch.finally”).
• Message Switch Center (MSC): Interconnects various scripts (content, worker, injected, and popup scripts) inherent in the Chrome extension structure. This facilitates smooth transitions from user input processing to LLM API calls and subsequent rendering of text in Overleaf.
• Dynamic Shortcut System: Utilizes the event bus to bind complex shortcut actions (e.g., "Control+Shift+B") to agent commands, ensuring that high-frequency operations such as content revision are efficiently handled.
  • Online Backend Integration for Auxiliary Services:

Beyond the frontend components, the solution includes an always-online backend driven by a Flask framework. This backend supports critical functionalities including:

• License activation and trial management.
• Real-time notifications.
• API key provisioning and validation.

The backend architecture not only ensures a reliable interface between OverleafCopilot and LLM providers but also incorporates robust privacy measures. Specifically, the design mandates that user content is not stored but merely routed to the LLM service provider, safeguarding user privacy during academic writing sessions.

• Extensive Use of Modern Web Technologies:

The development leverages JavaScript frameworks such as Vue and Vuetify to achieve a highly modular and component-based front end. This facilitates rapid development cycles while ensuring that the user interface remains highly customizable and responsive to diverse academic writing scenarios.

• Detailed Directive and Command Set:

The paper further elaborates on the directive sets available within the TDE. These include functionalities spanning:

• Basic utilities: Commands like join-diff and diff for text comparison.
• Agent-specific commands: prompt, system, user, pre-action, and post-action directives that structure the interaction flow with the LLM.
• Buffer management: Commands such as input and output to handle user text and model responses.
• UI control: Definitions for workspace elements (e.g., toolbars, text areas, keydown bindings) that allow dynamic rearrangement of the extension’s interface based on user needs.
• Overleaf integration: Specific commands to interface with the Overleaf API (e.g., text insertion, comment creation).

In summary, the paper details a novel technical framework that addresses the integration of state-of-the-art LLMs with a professional academic writing tool. It provides a comprehensive blueprint that spans the entire stack—from a front-end browser extension with sophisticated event handling, to a customizable and modular agent system anchored in a flexible templating language, and an online backend that manages ancillary operations securely. Numerical parameters, such as a typical temperature setting of 0.7 and a default maximum token count of 2000, underscore the quantitative precision in LLM configurations. The work is positioned to significantly enhance the efficiency and quality of academic writing, underpinned by a robust technical architecture that combines modern web development practices with advanced natural language processing capabilities.