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Exploring Generative Artificial Intelligence Techniques in Government: A Case Study
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AI
DOI：10.1109/MIS.2025.3569429.png)
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Abstract
En 中文
The swift progress of generative artificial intelligence (GenAI), notably large language models (LLMs), is reshaping the digital landscape. Recognizing this transformative potential, the National Research Council of Canada (NRC) launched a pilot initiative to explore the integration of GenAI techniques into its daily operation for performance excellence, where 22 projects were launched in May 2024. Within these projects, this article presents the development of the intelligent agent Pubbie as a case study, targeting the automation of performance measurement, data management, and insight reporting at the NRC. Cutting-edge techniques are explored, including LLM orchestration and semantic embedding via RoBERTa, while strategic fine-tuning and few-shot learning approaches are incorporated to infuse domain knowledge at an affordable cost. The user-friendly interface of Pubbie allows general government users to input queries in natural language and easily upload or download files with a simple button click, greatly reducing manual efforts and accessibility barriers.
Keywords:
Databases
Intelligent agents
Government
Semantics
Manuals
Few shot learning
Encoding
Bidirectional control
Performance evaluation
Generative AI
Artificial intelligence
Large language models
Project management