AI-Powered Document Generation: Using NLP for Intelligent Data-To-Template Mapping
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Abstract: Augmenting Automated Document Generation This paper introduces the Sandbox: Document Generating Engine, a novel, secure, and modular web application built with Python and Streamli (Achachlouei, A., Patil, M. A., Joshi, Q., Vair, T. & N. 2021). The primary research objective is to validate the feasibility and efficacy of augmenting Intelligent Document Processing (IDP) workflows by integrating Contemporary Large Language Models (LLMs) for semantic data-to-template mapping. Addressing the challenges of manual, time-consuming, and error-prone document creation, the system leverages Natural Language Processing (NLP) capabilities to analyze data uploaded in diverse formats (e.g., .csv, .xlsx, .txt) and automatically populate predefined document templates (Adhikari, P. R. 2018). The system features a robust secure authentication module utilizing bcrypt for password hashing and PostgreSQL for credential management. Our initial technical findings demonstrate high reliability, with Extraction Accuracy consistently over 95% across test documents. Furthermore, the system drastically reduced the time required for complex document creation, validating the capacity of LLM-enhanced IDP to yield substantial improvements in efficiency and productivity over simple rule-based methods. (Bitzenbauer, P. 2023).
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