Summary of Llm Based Multi-agent Generation Of Semi-structured Documents From Semantic Templates in the Public Administration Domain, by Emanuele Musumeci et al.
LLM Based Multi-Agent Generation of Semi-structured Documents from Semantic Templates in the Public Administration Domain
by Emanuele Musumeci, Michele Brienza, Vincenzo Suriani, Daniele Nardi, Domenico Daniele Bloisi
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach that combines Large Language Models (LLMs) with prompt engineering and multi-agent systems to generate new documents compliant with a desired structure. Specifically, it introduces a task description generated by semantic retrieval from an LLM to replace manual prompting, which is commonly used in Public Administration (PA) scenarios. The authors demonstrate the effectiveness of this approach through experiments and case studies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using computers to help people create documents more easily. Right now, it’s hard to make documents because they come in many different forms and styles. This makes it difficult for computers to understand what kind of information each document has. The authors have a new idea that uses special computer models called Large Language Models (LLMs) to help make this process easier. They want to create new documents that fit into specific formats, like a template. By using these LLMs and some special techniques, the authors hope to make it easier for people to create documents without having to do everything by hand. |
Keywords
» Artificial intelligence » Prompt » Prompting
