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

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dc.contributor.authorHeim, Desiree
dc.contributor.authorJilek, Christian
dc.contributor.authorUlges, Adrian
dc.contributor.authorDengel, Andreas
dc.contributor.editorKlein, Maike
dc.contributor.editorKrupka, Daniel
dc.contributor.editorWinter, Cornelia
dc.contributor.editorGergeleit, Martin
dc.contributor.editorMartin, Ludger
dc.date.accessioned2024-10-21T18:24:13Z
dc.date.available2024-10-21T18:24:13Z
dc.date.issued2024
dc.description.abstractCurrent 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.en
dc.identifier.doi10.18420/inf2024_118
dc.identifier.eissn2944-7682
dc.identifier.isbn978-3-88579-746-3
dc.identifier.issn2944-7682
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/45090
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofINFORMATIK 2024
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-352
dc.subjectKnowledge Work Dataset Generator
dc.subjectLarge Language Models
dc.subjectMulti-agent Simulation
dc.titleUsing Large Language Models to Generate Authentic Multi-agent Knowledge Work Datasetsen
dc.typeText/Conference Paper
gi.citation.endPage1357
gi.citation.publisherPlaceBonn
gi.citation.startPage1347
gi.conference.date24.-26. September 2024
gi.conference.locationWiesbaden
gi.conference.sessiontitleAI@WORK

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