This work package focuses on the use of Large Language Models (LLMs) within official statistics. The overarching goal is to explore and implement LLM technology to enhance data management, quality control, and allow for process automation.
The key objectives include:
Exploring LLM Applications: Identifying and integrating LLMs into the production of official statistics, determining the benefits of fine-tuning existing LLMs, and defining architectural enablers and constraints.
Iterative Approach: The work package will adopt a flexible methodology that aligns with the rapid development of LLM technology. This will allow for the selection and implementation of applications closer to their start dates.
The specific tasks are divided into three main areas:
T12.1 Architecture of Implementing LLM: This task focuses on integrating LLMs into production environments while ensuring data protection. It will provide guidance on using frameworks like Langchain to match LLMs with various requirements and constraints, analyzing the impact of using open source versus proprietary LLMs on reusability and data sensitivity.
T12.2 Use of Pre-existing LLM: The aim here is to develop at least two prototypes showcasing LLM applications in areas like data and metadata handling, draft text generation for analysis, production code improvement and translation, chatbot implementation for dissemination, and large document and web data analysis.
T12.3 Fine-tuning of Existing LLM for Specialized Statistical Applications: This involves customizing pre-trained LLMs to generate statistical insights and reports efficiently. The task aims to produce a prototype that enhances at least one high-value area, potentially laying the groundwork for a European Statistical Language Model.
Overall, the work package is designed to ensure that the integration and utilization of LLM technology are well-aligned with the evolving needs and standards of the official statistics community.
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