Exploring Generative AI Techniques in Government: A Case Study
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Abstract (arXiv)
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 paper 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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 23 references that are in the map. First posted on arXiv 6 Apr 2025.
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7 of 23 references.
References · 7
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
- 2023A Survey on Evaluation of Large Language Modelscited by 1,242
- 2023Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyondcited by 411
- 2023Generative AIcited by 120
- 2024A Survey on Employing Large Language Models for Text-to-SQL Taskscited by 67
- 2022Deep Learning Driven Natural Languages Text to SQL Query Conversion: A Surveycited by 11
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