Summary of Using Large Language Models to Generate Authentic Multi-agent Knowledge Work Datasets, by Desiree Heim et al.
Using Large Language Models to Generate Authentic Multi-agent Knowledge Work Datasets
by Desiree Heim, Christian Jilek, Adrian Ulges, Andreas Dengel
First submitted to arxiv on: 6 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 to creating datasets for evaluating and optimizing knowledge work assistance systems. The current publicly available data collections lack diversity, annotations, and user context, making it challenging to conduct objective evaluations. A configurable multi-agent dataset generator is proposed to simulate collaborative knowledge work among agents producing Large Language Model-generated documents and accompanying data traces. This system captures all background information in a knowledge graph, allowing for the creation of a high-quality dataset that can be shared without privacy concerns. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier to create datasets for systems that help people with their work. Right now, there aren’t many good datasets available because they lack important details and are hard to make. The researchers suggest creating a new kind of dataset by having virtual teams work together and generate documents. This way, the dataset will have all the information needed to test how well the system works. |
Keywords
» Artificial intelligence » Knowledge graph » Large language model
