# Using Large Language Models to Generate Authentic Multi-agent Knowledge Work Datasets

arXiv: https://arxiv.org/abs/2409.04286 · HTML: https://mlsanity.com/paper/2409.04286/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 9 of its 19 references that are in the map.
- First posted on arXiv 6 Sep 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, 9 of 9)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Large Language Model based Multi-Agents: A Survey of Progress and Challenges](https://mlsanity.com/paper/2402.01680/) (2024)
- [All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Text](https://mlsanity.com/paper/2107.00061/) (2021)
- [Human heuristics for AI-generated language are flawed](https://mlsanity.com/paper/2206.07271/) (2022)
- [Targeted Phishing Campaigns using Large Scale Language Models](https://mlsanity.com/paper/2301.00665/) (2022)
- [Human or Not? A Gamified Approach to the Turing Test](https://mlsanity.com/paper/2305.20010/) (2023)
- [Dataset Generation Patterns for Evaluating Knowledge Graph Construction](https://mlsanity.com/paper/2104.13576/) (2021)
- [Data Collection of Real-Life Knowledge Work in Context: The RLKWiC Dataset](https://mlsanity.com/paper/2404.10505/) (2024)

## Abstract (arXiv)

Current publicly available knowledge work data collections lack diversity, extensive annotations, and contextual information about the users and their documents. These issues hinder objective and comparable data-driven evaluations and optimizations of knowledge work assistance systems. Due to the considerable resources needed to collect such data in real-life settings and the necessity of data censorship, collecting such a dataset appears nearly impossible. For this reason, we propose a configurable, multi-agent knowledge work dataset generator. This system simulates collaborative knowledge work among agents producing Large Language Model-generated documents and accompanying data traces. Additionally, the generator captures all background information, given in its configuration or created during the simulation process, in a knowledge graph. Finally, the resulting dataset can be utilized and shared without privacy or confidentiality concerns. This paper introduces our approach's design and vision and focuses on generating authentic knowledge work documents using Large Language Models. Our study involving human raters who assessed 53% of the generated and 74% of the real documents as realistic demonstrates the potential of our approach. Furthermore, we analyze the authenticity criteria mentioned in the participants' comments and elaborate on potential improvements for identified common issues.