LLM-for-X: Application-agnostic Integration of Large Language Models to Support Personal Writing Workflows

TL;DR

LLM-for-X is a system that integrates large language models into various applications via a lightweight overlay, enhancing productivity by providing seamless, context-agnostic LLM assistance across diverse software environments.

Contribution

It introduces a universal, application-agnostic layer that connects LLMs to multiple software tools, enabling integrated assistance without changing existing workflows.

Findings

Improves user efficiency in writing and reading tasks.

Reduces context switching by embedding LLM support directly into applications.

Demonstrates compatibility with popular apps like Office, VSCode, and web platforms.

Abstract

To enhance productivity and to streamline workflows, there is a growing trend to embed large language model (LLM) functionality into applications, from browser-based web apps to native apps that run on personal computers. Here, we introduce LLM-for-X, a system-wide shortcut layer that seamlessly augments any application with LLM services through a lightweight popup dialog. Our native layer seamlessly connects front-end applications to popular LLM backends, such as ChatGPT and Gemini, using their uniform chat front-ends as the programming interface or their custom API calls. We demonstrate the benefits of LLM-for-X across a wide variety of applications, including Microsoft Office, VSCode, and Adobe Acrobat as well as popular web apps such as Overleaf. In our evaluation, we compared LLM-for-X with ChatGPT's web interface in a series of tasks, showing that our approach can provide users with quick, efficient, and easy-to-use LLM assistance without context switching to support writing and reading tasks that is agnostic of the specific application.