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PERSPECTIVE article

Front. Artif. Intell.
Sec. Machine Learning and Artificial Intelligence
Volume 7 - 2024 | doi: 10.3389/frai.2024.1464690
This article is part of the Research Topic Hybrid Human Artificial Intelligence: Augmenting Human Intelligence with AI View all 5 articles

Fostering Effective Hybrid Human-LLM Reasoning and Decision Making

Provisionally accepted
Andrea Passerini Andrea Passerini 1*Aryo Gema Aryo Gema 2Burcu Sayin Günel Burcu Sayin Günel 1Pasquale Minervini Pasquale Minervini 2Katya Tentori Katya Tentori 1
  • 1 University of Trento, Trento, Italy
  • 2 University of Edinburgh, Edinburgh, Scotland, United Kingdom

The final, formatted version of the article will be published soon.

    The impressive performance of modern Large Language Models (LLMs) across a wide range of tasks, along with their often non-trivial errors, has garnered unprecedented attention regarding the potential of AI and its impact on everyday life. While considerable effort has been and continues to be dedicated to overcoming the limitations of current models, the potentials and risks of human-LLM collaboration remain largely underexplored. In this perspective, we argue that enhancing the focus on human-LLM interaction should be a primary target for future LLM research. Specifically, we will briefly examine some of the biases that may hinder effective collaboration between humans and machines, explore potential solutions, and discuss two broader goals-mutual understanding and complementary team performance-that, in our view, future research should address to enhance effective human-LLM reasoning and decision-making.

    Keywords: Hybrid intelligence, human-AI collaboration, LLMS, biases, mutual understanding, complementary team performance

    Received: 14 Jul 2024; Accepted: 19 Dec 2024.

    Copyright: © 2024 Passerini, Gema, Sayin Günel, Minervini and Tentori. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Andrea Passerini, University of Trento, Trento, Italy

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.