Empowering Large Language Models in Hybrid Intelligence Systems through Data-Centric Process Models

Authors

  • Carsten Maletzki German Research Center for Artificial Intelligence (DFKI), Branch University of Trier, Behringstraße 21, 54296 Trier, Germany
  • Eric Rietzke German Research Center for Artificial Intelligence (DFKI), Branch University of Trier, Behringstraße 21, 54296 Trier, Germany LiveReader GmbH, Zur Imweiler Wies 3, 66649 Oberthal, Germany
  • Ralph Bergmann German Research Center for Artificial Intelligence (DFKI), Branch University of Trier, Behringstraße 21, 54296 Trier, Germany Artificial Intelligence and Intelligent Information Systems, University of Trier, Behringstraße 21, 54296 Trier, Germany

DOI:

https://doi.org/10.1609/aaaiss.v3i1.31196

Keywords:

Hybrid Intelligence, Data-Centric Process Modeling, Large Language Models, Human-AI Collaboration, Knowledge-Intensive Processes

Abstract

Hybrid intelligence systems aim to leverage synergies in closely collaborating teams of humans and artificial intelligence (AI). To guide the realization of such teams, recent research proposed design patterns that capture role-based knowledge on human-AI collaborations. Building on these patterns requires hybrid intelligence systems to provide mechanisms that orchestrate human and AI contributions accordingly. So far, it is unclear if such mechanisms can be provided based on shared representations of the required knowledge. In this regard, we expect ontology-based data-centric process modeling to be a promising direction for hybrid intelligence systems that aim to support knowledge-intensive processes (KiPs). We illustrate this through exemplary process models (realized with our ontology- and data-driven business process model -- ODD-BP) that reflect the team design patterns for hybrid intelligence systems. We point out that relying on such process models enables multiple actors to fulfill roles jointly and allows them to address individual shortcomings. This is examined by discussing integrating large language models (LLMs) into the process models and describing how complementary AI actors could help to empower LLMs to fulfill their role in human-AI collaboration more comprehensively. Future work will extend the provided concepts while their evaluation initially focuses on the KiP of medical emergency call handling.

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Published

2024-05-20

Issue

Section

Empowering Machine Learning and Large Language Models with Domain and Commonsense Knowledge