Natural-language querying of legislative processes and contents
Public engagement and open parliament
Italy - Senate
Use case ID: 029
Author: Senate of Italy
Date: 12 June 2024
Objective:
Enable users to query the progress and content of laws, bills and other parliamentary acts using natural language, improving accessibility and user experience by allowing intuitive, human-like interactions with the legislative database.
Actors:
- Senate website users (citizens, researchers and journalists)
- Senate IT and web development team
Prerequisites:
- Existing database of legislative processes and documents
- Trained large language model (LLM)-based artificial intelligence (AI) model for understanding and processing natural-language queries
- Internet accessibility for users
Scenario:
- The user accesses the Senate website.
- The user enters a query in natural language (e.g. “What is the current status of the education reform bill?”) in the search bar.
- The LLM-based AI model processes the natural-language query to understand the intent and key terms.
- The AI system retrieves relevant information from the legislative database, including the current status, key milestones and content of the specified bill or act.
- The results are displayed to the user in a user-friendly format, with options to view detailed progress or full texts.
- The user can refine their search or ask follow-up questions in natural language to obtain more specific information.
- The system logs the query and results for continuous improvement of the LLM model.
Alternate flows:
- If the LLM-based AI model cannot understand the query, it prompts the user to rephrase or provides suggestions.
- If the search yields too many or too few results, the system offers advanced search options or filters to refine the search.
Expected results:
- User satisfaction is improved owing to quick and accurate responses to queries.
- It takes less time and effort for users to find specific information on legislative processes and documents.
- Accessibility for users unfamiliar with legal terminology or the legislative process is improved.
Potential challenges:
- Ensuring the LLM-based AI model can accurately understand diverse phrasing and terminologies related to legislative processes
- Handling ambiguous queries or those with multiple possible interpretations
- Continuously updating the LLM-based AI model to adapt to new terms and changes in the legislative process
Data requirements:
- Database of current and past legislative processes, bills and acts
- Real-time user query data for ongoing learning and adaptation
Integrations with other systems:
- Existing legislative database and search infrastructure of the Senate website
- LLM-based AI processing systems and models
- User interface components for displaying query results
Success metrics:
- Query response time
- User satisfaction ratings and feedback
- Accuracy and relevance of query results
- Reduction in user queries requiring manual intervention or support