Automating Government Report Generation: A Generative AI Approach for Efficient Data Extraction, Analysis, and Visualization

This application paper introduces a transformative solution to address the labour-intensive manual report generation, data searching and report revision process in government entities. Traditional methods of data extraction, analysis, and graph creation for annual reports are not only time-consuming but also prone to human errors. To mitigate these challenges, we propose an innovative system leveraging Generative Artificial Intelligence (GenAI), with a specific focus on large language models (LLMs). Our solution incorporates automated data extraction from diverse sources designated as internal knowledge base, text analysis, summarization using advanced language models, and the generation as well as revisions of informative graphs. Different LLMs like Google's Gemini Pro and OpenAI's GPT 4.0 have been used to read different data visualisation graphs and fetch the information from the internal knowledge base, respectively, to update the graphs in an automated manner. Solution implementation using Python language on the World Economic Situation and Prospects report by the Department of Economic & Social Affairs, United Nation shows that the early result produces almost 0.87% to 17.46% average error rates in the task of factual data visualisation graph. Key benefits of this approach include improved time efficiency, consistency in report format, enhanced insights, and a user-friendly interface. A review and approval workflow facilitate user feedback, contributing to continuous model performance improvement.