LLM Based Multi-Agent Generation of Semi-structured Documents from
  Semantic Templates in the Public Administration Domain
Emanuele Musumeci Michele Brienza Vincenzo Suriani Daniele Nardi Domenico Daniele Bloisi
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.