How can LLMs automate government processes?
Exploring Generative AI Techniques in Government: A Case Study
April 16, 2025
https://arxiv.org/pdf/2504.10497This paper details the development of Pubbie, an intelligent agent using Large Language Models (LLMs) to automate tasks within the National Research Council of Canada (NRC), specifically linking research publications to NRC funding programs and answering user queries about these publications.
Key points for LLM-based multi-agent systems:
- LLM Orchestration: Pubbie utilizes multiple LLMs coordinated through Semantic Kernel to handle different sub-tasks like query classification, database interaction, and response generation.
- Few-Shot Learning with Prompt Templates: Domain-specific knowledge is injected into the LLMs via prompt templates and few-shot learning examples, improving accuracy and context-awareness.
- Integration with External Databases: Pubbie overcomes LLM input length limitations by integrating an SQLite database to store and retrieve publication data, enabling querying and updating through natural language.
- User-Friendly Interface: A Streamlit-based interface allows non-technical users to interact with the system easily via natural language and simple file uploads/downloads.
- Cost-Effective Solution: Pubbie is designed to operate within a resource-constrained environment, utilizing affordable computing resources and minimizing the need for specialized technical expertise.
- Collaborative Development Model: The project highlights a collaborative approach involving students, business process owners, and researchers, fostering innovation and shared problem-solving.