# LLM Based Multi-Agent Generation of Semi-structured Documents from Semantic Templates in the Public Administration Domain

arXiv: https://arxiv.org/abs/2402.14871 · HTML: https://mlsanity.com/paper/2402.14871/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 11 of its 16 references that are in the map.
- First posted on arXiv 21 Feb 2024.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 11 of 11)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [Benchmarking Large Language Models in Retrieval-Augmented Generation](https://mlsanity.com/paper/2309.01431/) (2023)
- [TabLLM: Few-shot Classification of Tabular Data with Large Language Models](https://mlsanity.com/paper/2210.10723/) (2022)
- [MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries](https://mlsanity.com/paper/2401.15391/) (2024)
- [Pretrained Language Models for Text Generation: A Survey](https://mlsanity.com/paper/2105.10311/) (2021)
- [Guiding Large Language Models via Directional Stimulus Prompting](https://mlsanity.com/paper/2302.11520/) (2023)
- [Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4](https://mlsanity.com/paper/2312.16171/) (2023)
- [LLM Harmony: Multi-Agent Communication for Problem Solving](https://mlsanity.com/paper/2401.01312/) (2024)
- [Embedding-based Retrieval with LLM for Effective Agriculture Information Extracting from Unstructured Data](https://mlsanity.com/paper/2308.03107/) (2023)

## Built on by (most cited first, 9 of 9)

- [From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future](https://mlsanity.com/paper/2408.02479/) (2024)
- [Multi-Agent Planning Using Visual Language Models](https://mlsanity.com/paper/2408.05478/) (2024)
- [LegalWiz: A Multi-Agent Generation Framework for Contradiction Detection in Legal Documents](https://mlsanity.com/paper/2510.03418/) (2025)
- [Turning Conversations into Workflows: A Framework to Extract and Evaluate Dialog Workflows for Service AI Agents](https://mlsanity.com/paper/2502.17321/) (2025)
- [EMPOWER: Embodied Multi-role Open-vocabulary Planning with Online Grounding and Execution](https://mlsanity.com/paper/2408.17379/) (2024)
- [Agent Benchmarks Fail Public Sector Requirements](https://mlsanity.com/paper/2601.20617/) (2026)
- [A Large Language Model-based Framework for Semi-Structured Tender Document Retrieval-Augmented Generation](https://mlsanity.com/paper/2410.09077/) (2024)
- [MATRIX: Multimodal Agent Tuning for Robust Tool-Use Reasoning](https://mlsanity.com/paper/2510.08567/) (2025)
- [Theme-Explanation Structure for Table Summarization using Large Language Models: A Case Study on Korean Tabular Data](https://mlsanity.com/paper/2501.10487/) (2025)

## Abstract (arXiv)

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.