ACL 2024system demonstrations2 citations

DocPilot: Copilot for Automating PDF Edit Workflows in Documents

Puneet Mathur, Alexa Siu, Varun Manjunatha, Tong Sun

Abstract

Digital documents, such as PDFs, are vital in business workflows, enabling communication, documentation, and collaboration. Handling PDFs can involve navigating complex workflows and numerous tools (e.g., comprehension, annotation, editing), which can be tedious and time-consuming for users. We introduce DocPilot, an AI-assisted document workflow Copilot system capable of understanding user intent and executing tasks accordingly to help users streamline their workflows. DocPilot undertakes intelligent orchestration of various tools through LLM prompting in four steps: (1) Task plan generation, (2) Task plan verification and self-correction, (3) Multi-turn User Feedback, and (4) Task Plan Execution via Code Generation and Error log-based Code Self-Revision. The primary goal of this system is to free the user from the intricacies of document editing, enabling them to focus on the creative aspects and enrich their document management experience.

BibTeX
@inproceedings{mathur-etal-2024-docpilot,
    title = "{D}oc{P}ilot: Copilot for Automating {PDF} Edit Workflows in Documents",
    author = "Mathur, Puneet  and
      Siu, Alexa  and
      Manjunatha, Varun  and
      Sun, Tong",
    editor = "Cao, Yixin  and
      Feng, Yang  and
      Xiong, Deyi",
    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.acl-demos.22/",
    doi = "10.18653/v1/2024.acl-demos.22",
    pages = "232--246"
}
DocPilot: Copilot for Automating PDF Edit Workflows in Documents · ACL 2024