LLM-for-X: Application-agnostic Integration of Large Language Models to Support Personal Writing Workflows
<|reference_start|>LLM-for-X: Application-agnostic Integration of Large Language Models to Support Personal Writing Workflows: 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.<|reference_end|>
@article{teufelberger2024llm-for-x:,
    title={LLM-for-X: Application-agnostic Integration of Large Language Models to
  Support Personal Writing Workflows},
    author={Lukas Teufelberger, Xintong Liu, Zhipeng Li, Max Moebus, Christian
  Holz},
    journal={arXiv preprint arXiv:2407.21593},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.21593},
    primaryClass={cs.HC}
}