LLM Based Multi-Agent Generation of Semi-structured Documents from Semantic Templates in the Public Administration Domain
This addresses the complexity of document creation in Public Administration, offering an incremental improvement by automating template generation.
The paper tackles the problem of generating semi-structured documents in Public Administration by proposing an LLM-based multi-agent system that replaces manual prompting with semantic retrieval, demonstrating effectiveness in real-world scenarios through experiments and case studies.
In the last years' digitalization process, the creation and management of documents in various domains, particularly in Public Administration (PA), have become increasingly complex and diverse. This complexity arises from the need to handle a wide range of document types, often characterized by semi-structured forms. Semi-structured documents present a fixed set of data without a fixed format. As a consequence, a template-based solution cannot be used, as understanding a document requires the extraction of the data structure. The recent introduction of Large Language Models (LLMs) has enabled the creation of customized text output satisfying user requests. In this work, we propose a novel approach that combines the LLMs with prompt engineering and multi-agent systems for generating new documents compliant with a desired structure. The main contribution of this work concerns replacing the commonly used manual prompting with a task description generated by semantic retrieval from an LLM. The potential of this approach is demonstrated through a series of experiments and case studies, showcasing its effectiveness in real-world PA scenarios.