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.- Anthology ID:
- 2024.acl-demos.22
- Volume:
- Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Yixin Cao, Yang Feng, Deyi Xiong
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 232–246
- Language:
- URL:
- https://aclanthology.org/2024.acl-demos.22
- DOI:
- Cite (ACL):
- Puneet Mathur, Alexa Siu, Varun Manjunatha, and Tong Sun. 2024. DocPilot: Copilot for Automating PDF Edit Workflows in Documents. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 232–246, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- DocPilot: Copilot for Automating PDF Edit Workflows in Documents (Mathur et al., ACL 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.acl-demos.22.pdf