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arXiv · 2407.21593

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

Lukas Teufelberger·Xintong Liu·Zhipeng Li·Max Moebus·Christian Holz
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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.

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Lukas Teufelberger, Xintong Liu, Zhipeng Li, Max Moebus, Christian Holz. 2024-07-31. LLM-for-X: Application-agnostic Integration of Large Language Models to Support Personal Writing Workflows. https://arxiv.org/abs/2407.21593

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