ArticleFrontiers in artificial intelligence2024
Fostering effective hybrid human-LLM reasoning and decision making.
Article in Frontiers in artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Toward a science of human-AI teaming for decision making: A complementarity framework.PNAS nexus · 2026Article
- Article
- Critical thinking in the age of generative AI: implications for health sciences education.Frontiers in artificial intelligence · 2025Article
- Artificial intelligence in nursing: advancing clinical judgment and decision-making.Revista brasileira de enfermagem · 2025Article
- An overview of model uncertainty and variability in LLM-based sentiment analysis: challenges, mitigation strategies, and the role of explainability.Frontiers in artificial intelligence · 2025Review
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Authors and funding
5 authors.
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Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.